WO2006127169A2 - Recognizing event patterns from event streams - Google Patents
Recognizing event patterns from event streams Download PDFInfo
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- WO2006127169A2 WO2006127169A2 PCT/US2006/013966 US2006013966W WO2006127169A2 WO 2006127169 A2 WO2006127169 A2 WO 2006127169A2 US 2006013966 W US2006013966 W US 2006013966W WO 2006127169 A2 WO2006127169 A2 WO 2006127169A2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
Definitions
- Embodiments of the present invention relate to the field of event stream processing.
- embodiments of this invention relate to identifying a pattern in a plurality of events.
- Business enterprises use computer systems with applications to monitor and process business activities and transactions.
- an online store selling purses may use a business application to receive online sales orders, an inventory application to manage the purses in the inventory and communicate with the supplier, or other applications or services to create online interfaces.
- Business entities handling complex transactions and activities may employ distributed computer systems. For example, financial institutions which handle credit card and ATM transactions may receive thousands of transactions every ten minutes.
- Each of the various activities or transactions may be treated as a single event by business applications or software.
- a user login session may be treated as an event and a consumer swiping her credit card at a shopping mall may be treated as another event by the financial institutions.
- each single event may be significant, analysis of each single event may not be beneficial. Instead, business entities or other organizations may be interested in occurrences of multiple events according to a specific pattern. Such accumulated information from the specific pattern represents a "higher level event" and is beneficial and meaningful for further analysis.
- single events such as a user's login session of an online store, placing an item in her shopping cart, or the like may be trivial.
- the online store may be interested in a specific pattern of (1) user login, (2) place item to the shopping cart, (3) proceed to check out, and (4) no further action from the user.
- Such pattern may indicate that the user has abandoned her shopping cart because (a) she is not pleased with the shipping and handling charges, (b) she decides to compare prices of the item before purchasing, (c) the user clicks the "Purchase" button, but the handling server did not receive the request, or other reasons.
- Current systems process events and attempt to match events to particular interested patterns.
- the online store may design an application to identify a given sequence of events, such as (1) user login, (2) shopping cart update, (3) checkout, and followed by no action from the user in the next ten minutes.
- pattern matching requires the online store to ask a software programmer to customize the application to handle the "abandoning of the shopping cart" pattern.
- pattern matching is accomplished as a hindsight process by performing analysis during offline period and/or only after data from events are stored in a database or data warehouse. [0006] These systems lack generic pattern recognition implementation to efficiently identify any given patterns from a series of events.
- customized codes or routines require existing systems to process correlation of event data and evaluation of patterns together.
- the existing systems wait for the occurrences of events to correlate data from events before determining whether a pattern has occurred.
- a new set of customized routines are needed to configure the existing system to evaluate the pattern.
- Embodiments of the present invention overcome shortcomings of the known systems by compiling a pattern description to define a series of events.
- a script or a set of executable code is attached or associated with each event and the script defines data parameters to correlate the events according to the pattern description.
- Embodiments of the invention execute script of each event to determine whether the event matches a particular pattern.
- a method identifies a pattern in a series of events.
- the method compiles a pattern description.
- the pattern description defines a series of a first event and a second event relating to the first event.
- the first event is received, and the first event includes a type parameter data, a time parameter data, and a substance parameter data.
- the method also attaches a first script to the first event.
- the first script defines type, time and substance parameters of the second event as a function of the parameters of the first event according to the pattern description.
- the attached first script of the first event is executed.
- the executed script identifies the second event and thereby identifies the pattern.
- one or more computer-readable media having computer-executable components identify event patterns.
- a pattern compiling component compiles a plurality of pattern descriptions. Each of the pattern descriptions defines a series of events in a sequence.
- An event component collects a plurality of events. Each of the plurality of events has data parameters relating to each of the plurality of events.
- a script component associates a script to each of the plurality of events. The script defines data parameters of a subsequent event in the series of each of the plurality of events according to the pattern description of each of the plurality of events.
- a pattern recognition engine executes the script of each of the plurality of events to identify the subsequent event in the series and thereby identify the pattern.
- a system identifies a pattern in a plurality of events.
- a plurality of collecting computers collects related events as a series of pattern events.
- a first set of computers identifies a first portion of the pattern in each collected series. Each of the collected series matches the first portion of the series of pattern events.
- a second set of computers identifies a second portion of the pattern in each series in which the first portion of the pattern has been identified by the first set. The first portion of the pattern and the second portion of the pattern match the series of pattern events.
- Each series of events including the first and second portions of the pattern is accumulated and stored for evaluation.
- the invention may comprise various other methods and apparatuses.
- FIG. 1 is a block diagram illustrating a system for identifying a pattern in a series of events according to one embodiment of the invention.
- FIGS. 2A-2F are diagrams illustrating an instance of a pattern recognition engine in the process of executing a script associated with an event according to one embodiment of the invention.
- FIG. 3 is a diagram illustrating a data structure for storing data relating to a pattern according to one embodiment of the invention.
- FIG. 4 is a diagram illustrating a data structure for organizing stored data in FIG. 3 for query according to one embodiment of the invention.
- FIG. 5 is a diagram illustrating a collection of cascaded servers for identifying a pattern in a series of events according to one embodiment of the invention.
- FIGS. 6A-6B are flow charts illustrating a method for identifying a pattern in a series of events implemented by a hosting environment according to one embodiment of the invention.
- FIG. 7 is a block diagram illustrating one example of a suitable computing system environment in which the invention may be implemented.
- Appendix A describes exemplary algorithms for implementing a method of one embodiment of the invention.
- Appendix B describes another implementation of a method of one embodiment of the invention.
- FIG. 1 a block diagram illustrates a system 100 for identifying a pattern in a series of events according to one embodiment of the invention.
- the system 100 includes a computing device (not shown in FIG. 1) such as the computer 130 in FIG. 7, including a computer server, a collection of computing devices linked by a common communication network (such as an internet, an intranet, or the like), or other computing system that is capable of executing a plurality of pattern recognition engines 112, and a script component 106.
- the system 100 includes an event component 102 for receiving a plurality of events 104.
- the event component 102 may include a user interface (UI) or an application programming interface (API) that receives a plurality of events 104 from an event source.
- UI user interface
- API application programming interface
- the event component 102 may receive events 104 as event streams from a web server that receives user login sessions, or the like.
- the event component 102 may receive the ATM transactions from ATM machines and the common communication network.
- the events 104 include occurrences of discrete steps of activities, such as ATM transactions, user login or interaction with a commercial web site, a user login session with a secured network, or the like.
- a non-occurrence of an activity may be represented by an event which is missing in the events 104.
- the system 100 also includes a pattern compiling component 110 responsive to compiling a pattern description, which defines a series of events in a sequence.
- the pattern description may define the pattern as (1) user login, (2) place item in the shopping cart, (3) checkout, and (4) no completion of the purchase of item(s) in the shopping cart in ten minutes.
- a pattern description of an "ATM fraud” may define a series of automatic teller machine (ATM) transactions as: (1) a first attempt to make an ATM transaction in location A by a customer's ATM card and (2) a second attempt to make another ATM transaction with the same card in different location B within ten minutes of the first attempt.
- ATM automatic teller machine
- Another example of the pattern description is to identify "theft or shoplifting" at a hardware store.
- items in a hardware store may be marked with a radio frequency identification (RPID) tag which contains information relating to the items, such as item number, item serial number, per unit price, or other metrics information of the item.
- RID radio frequency identification
- An RFID tag reader at a checkout counter emits radio signals to scan the presence of the RFID tags in the hardware store to register the items that are being purchased by the customer.
- Another RFID tag reader is also located at the exit of the hardware store to monitor the items as they leave the store.
- An interesting pattern in this situation is the hardware store's system indicates that the RFID tag reader at the exit scans an item's RFID tag showing that the tag has not been scanned at the checkout counter.
- Such a pattern may indicate a possible theft or shoplift.
- a further pattern description example may be illustrated by a "hacker attack" pattern where: (1) a successful virtual private network (VPN) login session is recorded, (2) no attempt to log-in to a domain is recorded, and (3) a failure or success attempt to log-in with the Telnet using the same IP address is recorded.
- VPN virtual private network
- Such pattern of events indicates that the domain is under a hacker attack or someone is attempting to access the domain without using the necessary client component.
- every event pattern can be decomposed in several pieces of information illustrated in Table 1:
- Embodiments of the present invention instead of analyzing data relating to events after they are being stored in data warehouse, watch or monitor the streams of events and evaluate the events in real-time to determine the patterns which indicate meaningful or useful information.
- the system 100 includes a script component that compiles the pattern description into set of small scripts, where each script describes the reaction on individual event type that may occur in the events 104.
- the script includes executable codes, expressions, routines, functions, conditions, or algorithms for indicating how to handle a particular event in order to find the next event in the pattern description.
- the script defines parameters of the next event as a function of the parameters of the particular event. Table 2 shows examples of operations included in the script.
- Table 2 Exemplary operations in the script.
- FIGS. 2A-2F are diagrams illustrating an instance of the pattern recognition engine 112 in the process of executing a script associated with an event 104 according to one embodiment of the invention.
- FIG. 2 A is a diagram illustrating interactions among an event 202, a script 210, and a pattern recognition engine (PRE) 218.
- PRE pattern recognition engine
- the event 202 includes parameter relating to an event type 204, an event time 206, and an event substance 208.
- the event type 204 may show "Withdraw,” the event time 206 as "9:55 P.M.,” and the event substance 208 as "Store0994.”
- other and additional parameter data for the event 202 may be included for each event 202.
- the script 210 includes one or more operations for the pattern recognition engine 112 to process according to the pattern description.
- the script 210 includes parts such as next event conditions 212 and data accumulation logic 214. As illustrated in Table 2, the script attached or associated with each event 202 indicates how to process subsequent events according to the pattern description (to be discussed in further details in FIGS. 2B-2F). Also shown in FIG. 2A, an instance of the PRE 218 is a generic script interpreter to execute the script 210 and contains the recognition state for individual pattern instance.
- the PRE 218 may be an executable module in the system 100 that performs execution of the script 210 such that the PRE 21 S receives the script 210 at a process event handle 216, processes the next event conditions at pattern event expectations 220, stores pattern instance data 222, and exposes properties according to the script 210 at "Status” 224, "Wakeup” 226, "JoinEvents” 228, and "GetData” 230.
- the PRE 218 executes the script and updates the status 224 as "Waiting" (i.e., waiting for the subsequent event).
- the PRE 218 also outputs the "Wakeup” 226 time of 10:05:20 to monitor the status 224.
- the "JoinEvents” or “GetHash” 228 describes the parameter data that needs to be correlated in the subsequent event and "GetData” 230 allows the external environment to retrieve the data accumulated so far.
- the PRE also exposes the method 217 "OnWakeup” which may change the status similar to merging a new event.
- the PRE 218 may be a set of computer-executable instructions that performs interpreting and executing the script 210 which may include one or more operations or routines according to the pattern description.
- embodiments of the present invention through the implementations of the script 210 and the PRE 218, are versatile in adapting to processing events and evaluating patterns of different conditions and parameters because the PRE 218 relies on an external environment (e.g., the script 210 and the event 202) to process relevant events 202 and scripts 210.
- the advantages of the embodiments of the invention at least eliminate the need for customized code or pattern processing for different users and different events because regardless of the types of events or types of patterns each user is interested in, the system 100 compiles the pattern description, associates the script that meets each user's needs so instances of the PRE 218 may execute the script to determine the occurrence of the pattern.
- a host environment including a computer, a computing system, a server, a plurality of servers, or other automated event receiving processes or applications provide the pattern description and/or the script for each event.
- the PRE 218 may be implemented as a small C# class programming language which may be implemented as user data type (UDT) or other programming languages, models, or the like.
- the small C# class may be responsible for holding the state of recognition of one individual instance of the pattern.
- the state may include the expected events and timeouts, and the data accumulated from the events, or other data according to the script 210.
- the event 202 may be passed to the PRE 218 also a C# class (UDT) containing the event type, the timestamp when the event happened (e.g., from the machine in which it happened), and some data relating to the substance of the event 202.
- the event 202 is passed to the PRE 218 through the routine OnEvent.
- OnEvent As an illustration, the "abandoning of the shopping cart" pattern is described in FIGS. 2B-2F. FIG.
- E2 stands for “Login”
- E2 stands for “ItemAdded”
- E3 stands for “Checkout”
- E4 stands for non-occurrence of the event “Pay.” If there is a series of events that contain these four events, the series may indicate that the user has abandoned her shopping cart.
- An event source 248 shows that events EVl, EV2, EV3 are currently waiting to be processed.
- the script attached to the event EVl 232 includes the following operations to process the event EVl 232: (a) an event type "ItemAdded” is to be expected within five minutes, (b) correlate this event with the event type "ItemAdded” based on "SessionID,” and (c) set data provided by the event EVl 232.
- the event EVl 232 is passed to the PRE 234 which executes the attached script of the event EVl 232.
- the PRE 234 indicates that an event type of "ItemAdded" needs to occur before an event time of
- the PRE 234 also needs to correlate the "SessionID" data of the EVl with the "ItemAdded” event so that they belong to the same series (i.e., the same user and a given login session).
- the script of the event EVl defines the type, time, and substance parameters of the next or expected event as a function of the parameters of the event EVl according to the pattern description. It is to be understood that other identifying data among the events may be used so as to correlate data among the events during execution of the script.
- the "Login" script for the event EVl is an activating event which indicates that it is the first event in a series.
- a snap shot 252 of the execution of the script by the PRE 234 shows that the event EVl 232 is the first event in a series A because the EVl is the first of the defined pattern (e.g., the "Login” event).
- the status of the series A is "Waiting” because the script indicates that an event "ItemAdded” is expected to occur by the same user under the same SessionID.
- the series A is discarded because the events no longer match the defined pattern description.
- the operation "WakeUp” in the script indicates the PRE 234 is to update the status of the series A to determine if the expected event has occurred.
- the GetHash(O) called on the PRE 234 will now return the hash of the SessionID.
- “SessionID” is also 589043543.
- the parameter data of the event EV2 corresponds to the expected event defined by the script of the event EVl
- the snapshot 258 of the pattern recognition processing shows what happens when the event EV2 belongs to the series A.
- the script associated with the event EV2 defines the next or expected event and the PRE 234' executes the script accordingly.
- the script of the event EV2 indicates that another "ItemAdded” event may occur within the next 5 minutes; the checkout time is to be determined and SessionID is used to correlate data with the next event.
- the PRE 234' shows the parameter data of the next event in the series A. For example, another "ItemAdded” event can occur before “10:05:20” (i.e., 10:00:20 + 5 minutes), and if the "Checkout” event does not occur before 10:05:20, the series A does not match the "abandoning of the shopping cart" pattern according to the pattern description.
- a new event EV3 240 is received from the event source 248 with the following parameter data: event type: "Login,” event time: “10:02:31,” and "SessionID:” 589077625.
- the script for "Login” indicates that this is an activating event
- the event EV3 240 is determined to start a new series B because the SessionID does not match that of the events EVl 232 and EV2 236.
- a snapshot 260 indicates that the event EV3 is the first event in the series B.
- script of the event EV3 defines a next event (e.g., "ItemAdded” to occur before 10:07:31,” or the like) and a new instance of the PRE 242 executes the script of the event EV3 240.
- another event EV4 256 is received with an event type of "Checkout," an event time of "10:02:40" and a SessionID of 589043543.
- the parameter data of this event EV4 256 matches the parameter data defined by the script of the event EV3 240.
- a snapshot 262 shows that the event EV4 belongs to the series A and it is the third event in the series A.
- the script of the event EV4 256 indicates that a "Pay" event is not expected in the next five minutes.
- the PRE 234" executes the script of the event EV4256 and indicates that the "Pay” event must not occur before "10:07:40.” Otherwise, the status of the series A is set to indicate that the series A does not match the pattern of "abandoning of the shopping cart.” [0043] Referring to FIG. 2F, the timeout that was set for "Pay” results in exposing the time "10:07:40" via the WakeupTime property. In addition the PRE 234'" remembers that if this timeout occurs, the new status should be "Match” as no more events are expected for the pattern. For example, a host environment (e.g., the host environment 114) that manages large number of PRE instances may keep a sorted list or an index based on WakeupTime.
- the index may be used to find all instances that need to be woken up. For example, at 10:08, it may be determined that the method OnWakeup of the PRE instance 234"' needs to be called. When this happens the PRE instance 234" sets its Status to "Match"which indicates to the host environment that the pattern of "abandoning of the shopping cart" is matched. [0044] In one embodiment, the PRE 234'" exposes the state of the event series and the data relating to the pattern to the host environment for analysis of the event series and the pattern. As an example such hosting environment can keep Hashtables based on GetHashQ and sorted list based on WakeupTime, both pointing to sets of PRE instances. This in-memory host environment may output the matched patterns as output events.
- the in-memory host environment keeps multiple instances of the engine in its storage, such as volatile, non-volatile, or a combination of volatile and non-volatile memory and delivers the incoming events and script operations (e.g., wakeup notifications) to the proper engine instances.
- the in-memory host environment also discards the irrelevant events and/or the PRE 234 having status as "NoMatch.”
- parameter data of the event series and the data relating to the pattern may be organized by serializing all instances of the PRE 234 in a collection of files or a file to allow recovery of the accumulated state in case of crash of the host environment, the PRE 234 or a combination thereof.
- a host environment may be an SQL-based host environment which implements the PRE 234 as a UDT (User Data Type - new feature of SQL 2005) so that script operations (e.g., timeout notifications) and large number of input events are efficiently delivered to large number of waiting instances of the PRE 234 using indexes on the properties accessible to the PRE 234.
- the SQL-based host environment may include a data-view of the incoming events or the event streams by promoting some or all of the data from the PRE 234 into SQL columns so that all the instances of event composition are visible and may be queried as a table (to be discussed in FIG. 3).
- the table or data structure storing the matched pattern events may be partitioned.
- FIG. 3 a diagram illustrates a data structure 312 for storing data relating to a pattern according to one embodiment of the invention.
- An event data structure stores events in a table 302.
- a script operation table 304 includes the pattern description with the script operation for each of the events to be identified.
- events are stored as UDTs in the table 302.
- the table 302 may include columns for the "Event Type” and the "Hash of the join criteria to join events.”
- the event type may be "Login,” “Checkout,” “ItemAdded,” or the like and the hash may be based on the SessionID.
- the columns in the table 302 may be included as a clustered index so as to optimize the physical storage of the table in disk sectors of the memory of computing systems (such as the computer 130 of FIG. 7).
- Deploying a new pattern type into such hosting environment may be achieved by inserting rows into table 304 that contain the scripts for the operations to be executed for each of the event types.
- the table 302 may be empty and as one ore more events are received from the event stream or event source 104, rows of the table 302 begin to fill with data relating to the events.
- the event table 302 and the script operation table 304 are correlated to the pattern table 312 where the patterns are evaluated and identified.
- the pattern table 312 includes information derived from the event table 302, the script operation table 304, and executed script information from the PRE.
- a SQL statement may define the pattern table 312 as: create table Patterns
- the pattern table 312 is sorted and organized by pattern types 306. For example, as shown in the pattern table 312, a pattern Pl is indexed above a pattern P2. Also as illustrated, the pattern Pl includes one or more pattern instances (as shown by rows within pattern Pl) where each row for each pattern instance indicates the pattern is waiting or expecting additional events. In addition to the index or sorting of pattern types, one embodiment of the invention organizes the pattern table by indexing the status column 308 and the wakeup column 310.
- instances of PRE execute the script according to the script operation table 304 to identify subsequent events in the series of pattern events to be evaluated.
- embodiments of the invention implement an algorithm to independently identify each pattern in parallel.
- one instance of the PRE may execute the script associated with events to evaluate the pattern Pl while another instance the PRE may execute the script associated with events to evaluate the pattern P2.
- Appendix B illustrates exemplary implementation of the algorithm and FIGS. 6 A and 6B illustrate an exemplary method derived from the algorithm.
- FIG. 4 is a diagram illustrating a data structure for organizing stored data in FIG. 3 for query according to one embodiment of the invention.
- a pattern table 402 includes a collection of patterns as a result of evaluating the pattern events.
- a collection 408 includes parameter data collected or extracted from the PRE instances, such as "Login", "Checkout", or the like.
- the pattern table 402 further includes information relating to the execution of the PRE.
- the matched patterns in the pattern table 402 are moved to a separate matched pattern table 404. As such, the matched pattern table 404 may be efficient searched or queried to further evaluate or remedy the transaction for the online purse store.
- FIG. 5 describes a diagram illustrating a collection of servers for identifying a pattern in a series of events according to one embodiment of the invention.
- a plurality of collecting computers 502 collects related events as a series of pattern events. For example, suppose the online purse store employs a number of front-end servers to collect data from user interactions with the online web site. These front-end servers are part of the collecting computers 502 that collect the events such as user login sessions, placing items in the user's shopping cart, or the like.
- Each of the collecting computers 502 may implement pattern descriptions, associating script to each of the events and executing the script using the PRE as described in FIGS. 1 and 2A-2F.
- a first set of computers 504 next identifies a first portion of the pattern in each collected series.
- Each of the collected series matches the first portion of the series of pattern events.
- a computer 512 may be configured to evaluate patterns (Pl, P2, and P3) while a computer 514 may be configured to evaluate or identify patterns (P4 and P5).
- These patterns P1-P5 may be regarded as events by a second set of computers 506.
- the second set of computers 506 receives the events (i.e., patterns P1-P5 from the first set 504) to identify a second portion of the pattern in each series.
- the first portion of the pattern identified by the first set 504 and the second portion of the pattern identified by the second set 506 are accumulated and stored for evaluation.
- the first set 504 may be regarded or treated as the collecting computers for the second set 506.
- a computer 516 of the second set 506 receives as events from both the computers 512 and 514 of the first set.
- a computer 518 in the second set 506 receives as events from the computer 514 of the first set 504 and from a computer 520 of the plurality of collecting computers 502.
- one or more hosting environments described may be a part of a distributed infrastructure for pattern matching on event-streams, so that some nodes (e.g., hosting environments) may b in-memory compositions of hosting environments, some may be file-based while others may be SQL- based hosting environments.
- data from the matched pattern and/or parameter data of the pattern events may be outputted to a host environment 508 (e.g., SQL-based host environment) or to a workflow business process analysis host environment for further processing of the matched pattern.
- a host environment 508 e.g., SQL-based host environment
- a workflow business process analysis host environment for further processing of the matched pattern.
- FIGS. 6A and 6B flow charts illustrating a method for identifying a pattern in a series of events implemented by a hosting environment according to one embodiment of the invention.
- Appendix A describes one implementation for identifying a pattern in a series of events. Initially, when a new event is received at 602, it is immediately or substantially immediately associated with a script at 604, based on the event type. At 606, different paths may be taken to determine whether the event should activate a new series. For example, as described in FIGS. 2A-2F, a "Login" event is an activating event because it is the first event in the series. If the event is activating, a new series is created at 608 and is added to the set of series that are waiting for events at 610. For example, in an in-memory hosting environment, this may be creating of a new PRE instance and registering it in a hashtable and a sorted list on wakeup time. In another embodiment where an SQL-based hosting environment is implemented, the SQL-based hosting environment may insert the PRE instance as UDT into the Patterns table. It next proceeds to receive one or more events at 602.
- the series that may be possibly interested in this event are to be processed at 612. For example, this may be achieved by first calling the OnE vent method of the PRE instance and passing the incoming event with the attached script.
- the PRE performs the next two operations at 614 by evaluating the pattern description. It is important to note that matching of the hash value does not necessarily mean that the correlation condition is satisfied. For example, suppose the hash was for some string that is to be used for correlation according to the pattern description, like social security number or name. As such, the PRE computes the actual pattern description against the data of the incoming event and the state accumulated in the series so far. If the evaluation of the pattern description is negative, the event is ignored for the series.
- the event is merged into the PRE.
- the status of the PRE instance may be changed or updated. For example, if a "Pay" event was received for the given
- SessionID the status changes to "No Match".
- the hosting environment determines what to do with this series. If the status is "Match" (e.g., a series of events has matched a pattern), the series is considered successfully complete and the series may be removed from the set of waiting instances and the accumulated data is sent as composite event at 620. Alternatively, the matched series may be kept for queries and analysis in another table as illustrated in FIG.4.
- the series may be discarded or ignored at 622.
- the status is "Waiting”
- the series is left to wait for more events by proceeding to 602.
- the event may not merge with any existing series or start a new series, but a copy or a clone of the series may be created before 616. As such, both a related or subsequent event and the cloned copy of the series are processed.
- Pl defines the following three events: Events A, B, and C where the x value of event A is equal to the x value of event B and the y value of event B is equal to the y value of event C.
- an event evl is received where the x value is 1 and does not have a y value. Based on the pattern description, the event evl is an activating event because it only includes an x value without an y value. As such, the event evl starts a new pattern series Sl. A second event, ev2, is received and includes an x value of 1 and an y value of 2. The pattern series Sl would identify the event ev2 as an event in the pattern Pl because the both x values of the events evl and ev2 are the same.
- the present invention clones or copies the series Sl which includes event evl such that there are two copies of the series: one copy is the original copy of the series Sl and the other copy will be used in a new series S2.
- the series Sl would have the event evl in the series.
- the new series S2 has the events evl and ev2.
- the series Sl would include the event ev3 because the x value of the event ev3 is equal to the x value of the evl .
- the series S2 would ignore the event ev3 because the y value of the event ev3 is not equal to that of the event ev2.
- the series Sl and S2 will evaluate the pattern description. With the cloned or copied series, the series Sl identifies the event ev4 as the event C and there is a matched pattern. On the other hand, the series S2 would not identify the event ev4 as the event C.
- the compilation of the pattern description and attaching of the event scripts determine whether cloning of the existing series may take place. For example when the series is waiting for non-occurrence, such as the event "Pay" in the shopping cart example, this type of event must be directly merged with no cloning, because the pattern description is to terminate the series. If, on the other hand, we are merging some event like the event B above, which includes the additional relationship or condition with the event C, the pattern description would require cloning of the series because it may take one series to identify the pattern.
- the waiting series may also be modified based on timer as illustrated in FIG.6B.
- a time period is calculated by a timer by keeping a timestamp of the last event from the event source.
- the event source may send signals on fixed intervals which will cause the algorithms described in Appendix A or Appendix B to execute.
- the operation of OnWakeup for each PRE instance is executed at 654.
- FIG. 7 shows one example of a general purpose computing device in the form of a computer 130.
- a computer such as the computer 130 is suitable for use in the other figures illustrated and described herein.
- Computer 130 has one or more processors or processing units 132 and a system memory 134.
- a system bus 136 couples various system components including the system memory 134 to the processors 132.
- the bus 136 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures.
- bus architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.
- ISA Industry Standard Architecture
- MCA Micro Channel Architecture
- EISA Enhanced ISA
- VESA Video Electronics Standards Association
- PCI Peripheral Component Interconnect
- the computer 130 typically has at least some form of computer readable media.
- Computer readable media which include both volatile and nonvolatile media, removable and non-removable media, may be any available medium that may be accessed by computer 130.
- Computer readable media comprise computer storage media and communication media.
- Computer storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.
- Computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store the desired information and that may be accessed by computer 130.
- Communication media typically embody computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media.
- a modulated data signal such as a carrier wave or other transport mechanism
- Wired media such as a wired network or direct-wired connection
- wireless media such as acoustic, RF, infrared, and other wireless media
- the system memory 134 includes computer storage media in the form of removable and/or non-removable, volatile and/or nonvolatile memory.
- system memory 134 includes read only memory (ROM) 138 and random access memory (RAM) 140.
- ROM read only memory
- RAM random access memory
- BIOS basic input/output system
- RAM 140 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 132.
- FIG. 7 illustrates operating system 144, application programs 146, other program modules 148, and program data 150.
- the computer 130 may also include other removable/non-removable, volatile/nonvolatile computer storage media.
- FIG. 7 illustrates a hard disk drive 154 that reads from or writes to non-removable, nonvolatile magnetic media.
- FIG. 7 also shows a magnetic disk drive 156 that reads from or writes to a removable, nonvolatile magnetic disk 158, and an optical disk drive 160 that reads from or writes to a removable, nonvolatile optical disk 162 such as a CD-ROM or other optical media.
- removable/non-removable, volatile/nonvolatile computer storage media that may be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like.
- the hard disk drive 154, and magnetic disk drive 156 and optical disk drive 160 are typically connected to the system bus 136 by a non- volatile memory interface, such as interface 166.
- the drives or other mass storage devices and their associated computer storage media discussed above and illustrated in FIG. 7, provide storage of computer readable instructions, data structures, program modules and other data for the computer 130.
- FIG. 7 The drives or other mass storage devices and their associated computer storage media discussed above and illustrated in FIG. 7, provide storage of computer readable instructions, data structures, program modules and other data for the computer 130.
- hard disk drive 154 is illustrated as storing operating system 170, application programs 172, other program modules 174, and program data 176. Note that these components may either be the same as or different from operating system 144, application programs 146, other program modules 148, and program data 150. Operating system 170, application programs 172, other program modules 174, and program data 176 are given different numbers here to illustrate that, at a minimum, they are different copies. [0073] A user may enter commands and information into computer 130 through input devices or user interface selection devices such as a keyboard 180 and a pointing device 182 (e.g., a mouse, trackball, pen, or touch pad).
- input devices or user interface selection devices such as a keyboard 180 and a pointing device 182 (e.g., a mouse, trackball, pen, or touch pad).
- Other input devices may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are connected to processing unit 132 through a user input interface 184 that is coupled to system bus 136, but may be connected by other interface and bus structures, such as a parallel port, game port, or a Universal Serial Bus (USB).
- a monitor 188 or other type of display device is also connected to system bus 136 via an interface, such as a video interface 190.
- computers often include other peripheral output devices (not shown) such as a printer and speakers, which may be connected through an output peripheral interface (not shown).
- the computer 130 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 194.
- the remote computer 194 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer 130.
- the logical connections depicted in FIG. 7 include a local area network (LAN) 196 and a wide area network (WAN) 198, but may also include other networks.
- LAN 136 and/or WAN 138 may be a wired network, a wireless network, a combination thereof, and so on.
- Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and global computer networks (e.g., the Internet).
- computer 130 When used in a local area networking environment, computer 130 is connected to the LAN 196 through a network interface or adapter 186.
- computer 130 When used in a wide area networking environment, computer 130 typically includes a modem 178 or other means for establishing communications over the WAN 198, such as the Internet.
- the modem 178 which may be internal or external, is connected to system bus 136 via the user input interface 184, or other appropriate mechanism.
- program modules depicted relative to computer 130, or portions thereof may be stored in a remote memory storage device (not shown).
- FIG. 7 illustrates remote application programs 192 as residing on the memory device.
- the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
- the data processors of computer 130 are programmed by means of instructions stored at different times in the various computer-readable storage media of the computer.
- Programs and operating systems are typically distributed, for example, on floppy disks or CD-ROMs. From there, they are installed or loaded into the secondary memory of a computer. At execution, they are loaded at least partially into the computer's primary electronic memory.
- the invention described herein includes these and other various types of computer-readable storage media when such media contain instructions or programs for implementing the steps described below in conjunction with a microprocessor or other data processor.
- the invention also includes the computer itself when programmed according to the methods and techniques described herein.
- programs and other executable program components such as the operating system, are illustrated herein as discrete blocks. It is recognized, however, that such programs and components reside at various times in different storage components of the computer, and are executed by the data processor(s) of the computer.
- Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, mobile telephones, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
- the invention may be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices.
- program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types.
- An interface in the context of a software architecture includes a software module, component, code portion, or other sequence of computer-executable instructions.
- the interface includes, for example, a first module accessing a second module to perform computing tasks on behalf of the first module.
- the first and second modules include, in one example, application programming interfaces (APIs) such as provided by operating systems, component object model (COM) interfaces (e.g., for peer-to-peer application communication), and extensible markup language metadata interchange format (XMI) interfaces (e.g., for communication between web services).
- APIs application programming interfaces
- COM component object model
- XMI extensible markup language metadata interchange format
- the interface may be a tightly coupled, synchronous implementation such as in Java 2 Platform Enterprise Edition (J2EE), COM, or distributed COM (DCOM) examples.
- the interface may be a loosely coupled, asynchronous implementation such as in a web service (e.g., using the simple object access protocol).
- the interface includes any combination of the following characteristics: tightly coupled, loosely coupled, synchronous, and asynchronous.
- the interface may conform to a standard protocol, a proprietary protocol, or any combination of standard and proprietary protocols.
- the interfaces described herein may all be part of a single interface or may be implemented as separate interfaces or any combination therein.
- the interfaces may execute locally or remotely to provide functionality. Further, the interfaces may include additional or less functionality than illustrated or described herein.
- computer 130 one or more computer-readable media having computer-executable components execute computer-executable instructions such as those illustrated in FIG. 6 to implement the invention.
- the algorithm begins with a loop for each event-type in the order or sequence expected by the pattern.
- the pattern description for the pattern Pl defines the pattern as have event types E1-E2-E3, and the script for the events El, E2 and E3 evaluates the parameter data.
- the script operation table 304 the following variables are defined for each event: @PatternType, @EventType, @IsActivating and @Script.
- the algorithm determines first if the Event type is activating or is part of one or more existing series of pattern events by providing the following exemplary SQL statement: INSERT Patterns(PatternType,PRE)
- a function InitializePRE is user defined function implemented in C# that accepts the activating event and the corresponding script.
- the columns in Hash, Status and Wakeup in the table are not set in the insert statement - they are byproduct of modifying the PRE .
- This statement can possibly result in thousands of new instances inserted - all in a single scan of the Event table and inserting on the clustered index on the Pattern table.
- the event type is not Activating, all the events are merged into the corresponding patterns with statement like:
- Hash is used just as heuristics - there is small probability that the PRE will ignore the event -e.g. on hash collision but different SessionID-s. Also, there may be cases in which more than one Hash column is needed — for example two separate Hash columns for will be needed for patterns like:
- each the merge logic will also use different type of join for each event type -e.g. join on x for e2 and join on y for e3. This means also that two pattern types can share the table only if they have the same number of joins.
- the next challenge is how to change the status of the patterns when the change is due to not seeing any events in the given timeout.
- This timeout-evaluation is achieved with another bulk-operation on the Patterns table, with SQL statement like: UPDATE Patterns SET PRE.OnWakeupQ
- WHERE PattemType @PatternType AND Wakeup ⁇ @Now FROM Patterns WITH(INDEX(Wakeup_Index))
- this operation uses the Wakeup_Index and thus only the disk sectors containing patterns that have reached timeout are retrieved and updated. There is no chance of Deadlock because this operation is localized to the part of the table for the specific Pattern Type and is not performed simultaneously with the Merge for this Pattern Type.
Abstract
Description
Claims
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Families Citing this family (136)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6985920B2 (en) * | 2003-06-23 | 2006-01-10 | Protego Networks Inc. | Method and system for determining intra-session event correlation across network address translation devices |
US7644365B2 (en) * | 2003-09-12 | 2010-01-05 | Cisco Technology, Inc. | Method and system for displaying network security incidents |
US7644079B2 (en) * | 2005-02-28 | 2010-01-05 | Gm Global Technology Operations, Inc. | System and method for temporal data mining |
US7937344B2 (en) | 2005-07-25 | 2011-05-03 | Splunk Inc. | Machine data web |
US7882262B2 (en) * | 2005-08-18 | 2011-02-01 | Cisco Technology, Inc. | Method and system for inline top N query computation |
US20070118545A1 (en) * | 2005-11-21 | 2007-05-24 | International Business Machines Corporation | Dynamic business process integration using complex event processing |
US7840575B2 (en) * | 2006-05-19 | 2010-11-23 | Oracle International Corporation | Evaluating event-generated data using append-only tables |
US8762395B2 (en) | 2006-05-19 | 2014-06-24 | Oracle International Corporation | Evaluating event-generated data using append-only tables |
US8131696B2 (en) * | 2006-05-19 | 2012-03-06 | Oracle International Corporation | Sequence event processing using append-only tables |
US10210071B1 (en) * | 2006-07-14 | 2019-02-19 | At&T Mobility Ip, Llc | Delta state tracking for event stream analysis |
WO2008043082A2 (en) | 2006-10-05 | 2008-04-10 | Splunk Inc. | Time series search engine |
US8122006B2 (en) * | 2007-05-29 | 2012-02-21 | Oracle International Corporation | Event processing query language including retain clause |
US7676461B2 (en) | 2007-07-18 | 2010-03-09 | Microsoft Corporation | Implementation of stream algebra over class instances |
US20090070765A1 (en) * | 2007-09-11 | 2009-03-12 | Bea Systems, Inc. | Xml-based configuration for event processing networks |
US9063979B2 (en) * | 2007-11-01 | 2015-06-23 | Ebay, Inc. | Analyzing event streams of user sessions |
US8549028B1 (en) | 2008-01-24 | 2013-10-01 | Case Global, Inc. | Incident tracking systems and methods |
US9753825B2 (en) | 2008-06-04 | 2017-09-05 | Oracle International Corporation | System and method for using an event window for testing an event processing system |
US10102091B2 (en) | 2008-06-04 | 2018-10-16 | Oracle International Corporation | System and method for supporting a testing framework for an event processing system using multiple input event streams |
US20100088325A1 (en) | 2008-10-07 | 2010-04-08 | Microsoft Corporation | Streaming Queries |
US8260912B2 (en) * | 2008-11-21 | 2012-09-04 | The Invention Science Fund I, Llc | Hypothesis based solicitation of data indicating at least one subjective user state |
US8028063B2 (en) * | 2008-11-21 | 2011-09-27 | The Invention Science Fund I, Llc | Soliciting data indicating at least one objective occurrence in response to acquisition of data indicating at least one subjective user state |
US8032628B2 (en) * | 2008-11-21 | 2011-10-04 | The Invention Science Fund I, Llc | Soliciting data indicating at least one objective occurrence in response to acquisition of data indicating at least one subjective user state |
US8224956B2 (en) | 2008-11-21 | 2012-07-17 | The Invention Science Fund I, Llc | Hypothesis selection and presentation of one or more advisories |
US8010663B2 (en) * | 2008-11-21 | 2011-08-30 | The Invention Science Fund I, Llc | Correlating data indicating subjective user states associated with multiple users with data indicating objective occurrences |
US8180830B2 (en) * | 2008-11-21 | 2012-05-15 | The Invention Science Fund I, Llc | Action execution based on user modified hypothesis |
US8244858B2 (en) * | 2008-11-21 | 2012-08-14 | The Invention Science Fund I, Llc | Action execution based on user modified hypothesis |
US8127002B2 (en) * | 2008-11-21 | 2012-02-28 | The Invention Science Fund I, Llc | Hypothesis development based on user and sensing device data |
US8224842B2 (en) | 2008-11-21 | 2012-07-17 | The Invention Science Fund I, Llc | Hypothesis selection and presentation of one or more advisories |
US8086668B2 (en) * | 2008-11-21 | 2011-12-27 | The Invention Science Fund I, Llc | Hypothesis based solicitation of data indicating at least one objective occurrence |
US8180890B2 (en) * | 2008-11-21 | 2012-05-15 | The Invention Science Fund I, Llc | Hypothesis based solicitation of data indicating at least one subjective user state |
US8239488B2 (en) * | 2008-11-21 | 2012-08-07 | The Invention Science Fund I, Llc | Hypothesis development based on user and sensing device data |
US8005948B2 (en) * | 2008-11-21 | 2011-08-23 | The Invention Science Fund I, Llc | Correlating subjective user states with objective occurrences associated with a user |
US8260729B2 (en) * | 2008-11-21 | 2012-09-04 | The Invention Science Fund I, Llc | Soliciting data indicating at least one subjective user state in response to acquisition of data indicating at least one objective occurrence |
US8046455B2 (en) * | 2008-11-21 | 2011-10-25 | The Invention Science Fund I, Llc | Correlating subjective user states with objective occurrences associated with a user |
US20100131607A1 (en) * | 2008-11-21 | 2010-05-27 | Searete Llc, A Limited Liability Corporation Of The State Of Delaware | Correlating data indicating subjective user states associated with multiple users with data indicating objective occurrences |
US8103613B2 (en) * | 2008-11-21 | 2012-01-24 | The Invention Science Fund I, Llc | Hypothesis based solicitation of data indicating at least one objective occurrence |
US8010662B2 (en) * | 2008-11-21 | 2011-08-30 | The Invention Science Fund I, Llc | Soliciting data indicating at least one subjective user state in response to acquisition of data indicating at least one objective occurrence |
US9158816B2 (en) | 2009-10-21 | 2015-10-13 | Microsoft Technology Licensing, Llc | Event processing with XML query based on reusable XML query template |
US9805101B2 (en) | 2010-02-26 | 2017-10-31 | Ebay Inc. | Parallel data stream processing system |
US8396815B2 (en) * | 2010-04-29 | 2013-03-12 | International Business Machines Corporation | Adaptive business process automation |
US20110302264A1 (en) * | 2010-06-02 | 2011-12-08 | International Business Machines Corporation | Rfid network to support processing of rfid data captured within a network domain |
US20130325743A1 (en) * | 2012-06-01 | 2013-12-05 | Northwest Research, Inc. | Systems and methods for tracking packages |
US9411327B2 (en) | 2012-08-27 | 2016-08-09 | Johnson Controls Technology Company | Systems and methods for classifying data in building automation systems |
US10997191B2 (en) | 2013-04-30 | 2021-05-04 | Splunk Inc. | Query-triggered processing of performance data and log data from an information technology environment |
US10318541B2 (en) | 2013-04-30 | 2019-06-11 | Splunk Inc. | Correlating log data with performance measurements having a specified relationship to a threshold value |
US10614132B2 (en) | 2013-04-30 | 2020-04-07 | Splunk Inc. | GUI-triggered processing of performance data and log data from an information technology environment |
US10346357B2 (en) | 2013-04-30 | 2019-07-09 | Splunk Inc. | Processing of performance data and structure data from an information technology environment |
US10019496B2 (en) | 2013-04-30 | 2018-07-10 | Splunk Inc. | Processing of performance data and log data from an information technology environment by using diverse data stores |
US10353957B2 (en) | 2013-04-30 | 2019-07-16 | Splunk Inc. | Processing of performance data and raw log data from an information technology environment |
US10225136B2 (en) | 2013-04-30 | 2019-03-05 | Splunk Inc. | Processing of log data and performance data obtained via an application programming interface (API) |
US9934279B2 (en) * | 2013-12-05 | 2018-04-03 | Oracle International Corporation | Pattern matching across multiple input data streams |
US9380068B2 (en) | 2014-08-18 | 2016-06-28 | Bank Of America Corporation | Modification of computing resource behavior based on aggregated monitoring information |
US9443192B1 (en) | 2015-08-30 | 2016-09-13 | Jasmin Cosic | Universal artificial intelligence engine for autonomous computing devices and software applications |
US10534326B2 (en) | 2015-10-21 | 2020-01-14 | Johnson Controls Technology Company | Building automation system with integrated building information model |
US11268732B2 (en) | 2016-01-22 | 2022-03-08 | Johnson Controls Technology Company | Building energy management system with energy analytics |
US11947785B2 (en) | 2016-01-22 | 2024-04-02 | Johnson Controls Technology Company | Building system with a building graph |
US9582762B1 (en) | 2016-02-05 | 2017-02-28 | Jasmin Cosic | Devices, systems, and methods for learning and using artificially intelligent interactive memories |
WO2017165708A1 (en) | 2016-03-23 | 2017-09-28 | FogHorn Systems, Inc. | Efficient state machines for real-time dataflow programming |
CN109154802A (en) | 2016-03-31 | 2019-01-04 | 江森自控科技公司 | HVAC device registration in distributed building management system |
US10505756B2 (en) | 2017-02-10 | 2019-12-10 | Johnson Controls Technology Company | Building management system with space graphs |
US11774920B2 (en) | 2016-05-04 | 2023-10-03 | Johnson Controls Technology Company | Building system with user presentation composition based on building context |
US10417451B2 (en) | 2017-09-27 | 2019-09-17 | Johnson Controls Technology Company | Building system with smart entity personal identifying information (PII) masking |
US10536476B2 (en) | 2016-07-21 | 2020-01-14 | Sap Se | Realtime triggering framework |
US9864933B1 (en) | 2016-08-23 | 2018-01-09 | Jasmin Cosic | Artificially intelligent systems, devices, and methods for learning and/or using visual surrounding for autonomous object operation |
US10482241B2 (en) | 2016-08-24 | 2019-11-19 | Sap Se | Visualization of data distributed in multiple dimensions |
US10542016B2 (en) | 2016-08-31 | 2020-01-21 | Sap Se | Location enrichment in enterprise threat detection |
US10331693B1 (en) | 2016-09-12 | 2019-06-25 | Amazon Technologies, Inc. | Filters and event schema for categorizing and processing streaming event data |
US10187251B1 (en) * | 2016-09-12 | 2019-01-22 | Amazon Technologies, Inc. | Event processing architecture for real-time member engagement |
US10673879B2 (en) * | 2016-09-23 | 2020-06-02 | Sap Se | Snapshot of a forensic investigation for enterprise threat detection |
US10630705B2 (en) | 2016-09-23 | 2020-04-21 | Sap Se | Real-time push API for log events in enterprise threat detection |
US10452974B1 (en) | 2016-11-02 | 2019-10-22 | Jasmin Cosic | Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation |
US10621599B1 (en) | 2016-12-02 | 2020-04-14 | Worldpay, Llc | Systems and methods for computer analytics of associations between online and offline purchase events |
US10534908B2 (en) | 2016-12-06 | 2020-01-14 | Sap Se | Alerts based on entities in security information and event management products |
US10534907B2 (en) | 2016-12-15 | 2020-01-14 | Sap Se | Providing semantic connectivity between a java application server and enterprise threat detection system using a J2EE data |
US10530792B2 (en) | 2016-12-15 | 2020-01-07 | Sap Se | Using frequency analysis in enterprise threat detection to detect intrusions in a computer system |
US11470094B2 (en) | 2016-12-16 | 2022-10-11 | Sap Se | Bi-directional content replication logic for enterprise threat detection |
US10552605B2 (en) | 2016-12-16 | 2020-02-04 | Sap Se | Anomaly detection in enterprise threat detection |
US10764306B2 (en) | 2016-12-19 | 2020-09-01 | Sap Se | Distributing cloud-computing platform content to enterprise threat detection systems |
US10607134B1 (en) | 2016-12-19 | 2020-03-31 | Jasmin Cosic | Artificially intelligent systems, devices, and methods for learning and/or using an avatar's circumstances for autonomous avatar operation |
US10684033B2 (en) | 2017-01-06 | 2020-06-16 | Johnson Controls Technology Company | HVAC system with automated device pairing |
US10496467B1 (en) | 2017-01-18 | 2019-12-03 | Amazon Technologies, Inc. | Monitoring software computations of arbitrary length and duration |
US10628278B2 (en) * | 2017-01-26 | 2020-04-21 | International Business Machines Corporation | Generation of end-user sessions from end-user events identified from computer system logs |
US11900287B2 (en) | 2017-05-25 | 2024-02-13 | Johnson Controls Tyco IP Holdings LLP | Model predictive maintenance system with budgetary constraints |
US10452043B2 (en) | 2017-02-10 | 2019-10-22 | Johnson Controls Technology Company | Building management system with nested stream generation |
US11764991B2 (en) | 2017-02-10 | 2023-09-19 | Johnson Controls Technology Company | Building management system with identity management |
US11360447B2 (en) | 2017-02-10 | 2022-06-14 | Johnson Controls Technology Company | Building smart entity system with agent based communication and control |
US10095756B2 (en) | 2017-02-10 | 2018-10-09 | Johnson Controls Technology Company | Building management system with declarative views of timeseries data |
US20190361412A1 (en) | 2017-02-10 | 2019-11-28 | Johnson Controls Technology Company | Building smart entity system with agent based data ingestion and entity creation using time series data |
US11307538B2 (en) | 2017-02-10 | 2022-04-19 | Johnson Controls Technology Company | Web services platform with cloud-eased feedback control |
US10515098B2 (en) | 2017-02-10 | 2019-12-24 | Johnson Controls Technology Company | Building management smart entity creation and maintenance using time series data |
US10854194B2 (en) | 2017-02-10 | 2020-12-01 | Johnson Controls Technology Company | Building system with digital twin based data ingestion and processing |
US11280509B2 (en) | 2017-07-17 | 2022-03-22 | Johnson Controls Technology Company | Systems and methods for agent based building simulation for optimal control |
WO2018175912A1 (en) | 2017-03-24 | 2018-09-27 | Johnson Controls Technology Company | Building management system with dynamic channel communication |
US11327737B2 (en) | 2017-04-21 | 2022-05-10 | Johnson Controls Tyco IP Holdings LLP | Building management system with cloud management of gateway configurations |
US10788229B2 (en) | 2017-05-10 | 2020-09-29 | Johnson Controls Technology Company | Building management system with a distributed blockchain database |
US11022947B2 (en) | 2017-06-07 | 2021-06-01 | Johnson Controls Technology Company | Building energy optimization system with economic load demand response (ELDR) optimization and ELDR user interfaces |
WO2018232147A1 (en) | 2017-06-15 | 2018-12-20 | Johnson Controls Technology Company | Building management system with artificial intelligence for unified agent based control of building subsystems |
US10530794B2 (en) | 2017-06-30 | 2020-01-07 | Sap Se | Pattern creation in enterprise threat detection |
US11733663B2 (en) | 2017-07-21 | 2023-08-22 | Johnson Controls Tyco IP Holdings LLP | Building management system with dynamic work order generation with adaptive diagnostic task details |
US20190034066A1 (en) | 2017-07-27 | 2019-01-31 | Johnson Controls Technology Company | Building management system with central plantroom dashboards |
US11258683B2 (en) | 2017-09-27 | 2022-02-22 | Johnson Controls Tyco IP Holdings LLP | Web services platform with nested stream generation |
US11314788B2 (en) | 2017-09-27 | 2022-04-26 | Johnson Controls Tyco IP Holdings LLP | Smart entity management for building management systems |
US10565844B2 (en) | 2017-09-27 | 2020-02-18 | Johnson Controls Technology Company | Building risk analysis system with global risk dashboard |
US10962945B2 (en) | 2017-09-27 | 2021-03-30 | Johnson Controls Technology Company | Building management system with integration of data into smart entities |
US11768826B2 (en) | 2017-09-27 | 2023-09-26 | Johnson Controls Tyco IP Holdings LLP | Web services for creation and maintenance of smart entities for connected devices |
US11321673B2 (en) | 2017-11-01 | 2022-05-03 | Samsung Electronics Co., Ltd. | Method and system for automatically creating an instant ad-hoc calendar event |
US11281169B2 (en) | 2017-11-15 | 2022-03-22 | Johnson Controls Tyco IP Holdings LLP | Building management system with point virtualization for online meters |
US10809682B2 (en) | 2017-11-15 | 2020-10-20 | Johnson Controls Technology Company | Building management system with optimized processing of building system data |
US10102449B1 (en) | 2017-11-21 | 2018-10-16 | Jasmin Cosic | Devices, systems, and methods for use in automation |
US11127235B2 (en) | 2017-11-22 | 2021-09-21 | Johnson Controls Tyco IP Holdings LLP | Building campus with integrated smart environment |
US10474934B1 (en) | 2017-11-26 | 2019-11-12 | Jasmin Cosic | Machine learning for computing enabled systems and/or devices |
US10402731B1 (en) | 2017-12-15 | 2019-09-03 | Jasmin Cosic | Machine learning for computer generated objects and/or applications |
US10681064B2 (en) | 2017-12-19 | 2020-06-09 | Sap Se | Analysis of complex relationships among information technology security-relevant entities using a network graph |
US10986111B2 (en) | 2017-12-19 | 2021-04-20 | Sap Se | Displaying a series of events along a time axis in enterprise threat detection |
US11954713B2 (en) | 2018-03-13 | 2024-04-09 | Johnson Controls Tyco IP Holdings LLP | Variable refrigerant flow system with electricity consumption apportionment |
US11403539B2 (en) | 2018-06-28 | 2022-08-02 | International Business Machines Corporation | Pattern-optimized session logs for improved web analytics |
US11016648B2 (en) | 2018-10-30 | 2021-05-25 | Johnson Controls Technology Company | Systems and methods for entity visualization and management with an entity node editor |
US20200162280A1 (en) | 2018-11-19 | 2020-05-21 | Johnson Controls Technology Company | Building system with performance identification through equipment exercising and entity relationships |
US11769117B2 (en) | 2019-01-18 | 2023-09-26 | Johnson Controls Tyco IP Holdings LLP | Building automation system with fault analysis and component procurement |
US10788798B2 (en) | 2019-01-28 | 2020-09-29 | Johnson Controls Technology Company | Building management system with hybrid edge-cloud processing |
CN110096363B (en) * | 2019-04-29 | 2021-11-30 | 亚信科技(成都)有限公司 | Method and device for associating network event with process |
KR102244450B1 (en) * | 2019-11-21 | 2021-04-26 | 인하대학교 산학협력단 | Recency-based pattern mining method in multiple event sequences |
US20210200807A1 (en) | 2019-12-31 | 2021-07-01 | Johnson Controls Technology Company | Building data platform with a graph change feed |
US11894944B2 (en) | 2019-12-31 | 2024-02-06 | Johnson Controls Tyco IP Holdings LLP | Building data platform with an enrichment loop |
US11537386B2 (en) | 2020-04-06 | 2022-12-27 | Johnson Controls Tyco IP Holdings LLP | Building system with dynamic configuration of network resources for 5G networks |
US11874809B2 (en) | 2020-06-08 | 2024-01-16 | Johnson Controls Tyco IP Holdings LLP | Building system with naming schema encoding entity type and entity relationships |
US11397773B2 (en) | 2020-09-30 | 2022-07-26 | Johnson Controls Tyco IP Holdings LLP | Building management system with semantic model integration |
US11954154B2 (en) | 2020-09-30 | 2024-04-09 | Johnson Controls Tyco IP Holdings LLP | Building management system with semantic model integration |
US20220138362A1 (en) | 2020-10-30 | 2022-05-05 | Johnson Controls Technology Company | Building management system with configuration by building model augmentation |
EP4309013A1 (en) | 2021-03-17 | 2024-01-24 | Johnson Controls Tyco IP Holdings LLP | Systems and methods for determining equipment energy waste |
US11769066B2 (en) | 2021-11-17 | 2023-09-26 | Johnson Controls Tyco IP Holdings LLP | Building data platform with digital twin triggers and actions |
US11899723B2 (en) | 2021-06-22 | 2024-02-13 | Johnson Controls Tyco IP Holdings LLP | Building data platform with context based twin function processing |
US11796974B2 (en) | 2021-11-16 | 2023-10-24 | Johnson Controls Tyco IP Holdings LLP | Building data platform with schema extensibility for properties and tags of a digital twin |
US11934966B2 (en) | 2021-11-17 | 2024-03-19 | Johnson Controls Tyco IP Holdings LLP | Building data platform with digital twin inferences |
US11704311B2 (en) | 2021-11-24 | 2023-07-18 | Johnson Controls Tyco IP Holdings LLP | Building data platform with a distributed digital twin |
US11714930B2 (en) | 2021-11-29 | 2023-08-01 | Johnson Controls Tyco IP Holdings LLP | Building data platform with digital twin based inferences and predictions for a graphical building model |
Family Cites Families (44)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5276873A (en) | 1988-12-22 | 1994-01-04 | Hughes Aircraft Company | Apparatus and method for generating capture commands for data acquisition |
US5794053A (en) | 1994-05-18 | 1998-08-11 | Bell Communications Research, Inc. | Method and system for dynamic interface contract creation |
US5549115A (en) | 1994-09-28 | 1996-08-27 | Heartstream, Inc. | Method and apparatus for gathering event data using a removable data storage medium and clock |
US5634127A (en) | 1994-11-30 | 1997-05-27 | International Business Machines Corporation | Methods and apparatus for implementing a message driven processor in a client-server environment |
GB9725347D0 (en) * | 1997-11-28 | 1998-01-28 | Ncr Int Inc | Database relationship analysis and strategy implementation tool |
US7233952B1 (en) | 1999-01-15 | 2007-06-19 | Hon Hai Precision Industry, Ltd. | Apparatus for visualizing information in a data warehousing environment |
US6411961B1 (en) | 1999-01-15 | 2002-06-25 | Metaedge Corporation | Apparatus for providing a reverse star schema data model |
CA2281331A1 (en) | 1999-09-03 | 2001-03-03 | Cognos Incorporated | Database management system |
US20040078464A1 (en) | 1999-09-16 | 2004-04-22 | Rajan Sreeranga P. | Method and apparatus for enabling real time monitoring and notification of data updates for WEB-based data synchronization services |
US6898790B1 (en) | 1999-12-06 | 2005-05-24 | International Business Machines Corporation | Mapping actions to tasks within customer service processing systems |
US20020062237A1 (en) | 2000-08-04 | 2002-05-23 | Scott Matsumoto | Transactional monitoring system and method |
US6708186B1 (en) | 2000-08-14 | 2004-03-16 | Oracle International Corporation | Aggregating and manipulating dictionary metadata in a database system |
US6895438B1 (en) | 2000-09-06 | 2005-05-17 | Paul C. Ulrich | Telecommunication-based time-management system and method |
US7111010B2 (en) | 2000-09-25 | 2006-09-19 | Hon Hai Precision Industry, Ltd. | Method and system for managing event attributes |
US20030208392A1 (en) | 2000-10-27 | 2003-11-06 | Manugistics, Inc. | Optimizing resource plans |
US20030033180A1 (en) | 2000-10-27 | 2003-02-13 | Manugistics, Inc. | System and method for optimizing resource plans |
GB0029226D0 (en) | 2000-11-30 | 2001-01-17 | Ebbon Dacs Ltd | Improvements relating to information systems |
US20020147622A1 (en) | 2000-12-18 | 2002-10-10 | Manugistics, Inc. | System and method for enabling a configurable electronic business exchange platform |
US20020138316A1 (en) | 2001-03-23 | 2002-09-26 | Katz Steven Bruce | Value chain intelligence system and methods |
US7120896B2 (en) | 2001-10-31 | 2006-10-10 | Vitria Technology, Inc. | Integrated business process modeling environment and models created thereby |
US6697810B2 (en) | 2001-04-19 | 2004-02-24 | Vigilance, Inc. | Security system for event monitoring, detection and notification system |
US20030018643A1 (en) | 2001-06-19 | 2003-01-23 | Peiwei Mi | VIGIP006 - collaborative resolution and tracking of detected events |
US20050086584A1 (en) * | 2001-07-09 | 2005-04-21 | Microsoft Corporation | XSL transform |
JP4047053B2 (en) * | 2002-04-16 | 2008-02-13 | 富士通株式会社 | Retrieval apparatus and method using sequence pattern including repetition |
US7107340B2 (en) | 2002-05-31 | 2006-09-12 | Microsoft Corporation | System and method for collecting and storing event data from distributed transactional applications |
AU2003259453A1 (en) | 2002-07-19 | 2004-02-09 | Sap Aktiengesellschaft | Business solution management (bsm) |
US7711670B2 (en) | 2002-11-13 | 2010-05-04 | Sap Ag | Agent engine |
US7437675B2 (en) | 2003-02-03 | 2008-10-14 | Hewlett-Packard Development Company, L.P. | System and method for monitoring event based systems |
JP2004240766A (en) * | 2003-02-06 | 2004-08-26 | Toshiba Corp | System and method for generating pattern detection processing program |
CA2418568C (en) | 2003-02-10 | 2011-10-11 | Watchfire Corporation | Method and system for classifying content and prioritizing web site content issues |
US7487148B2 (en) * | 2003-02-28 | 2009-02-03 | Eaton Corporation | System and method for analyzing data |
US7114146B2 (en) | 2003-05-02 | 2006-09-26 | International Business Machines Corporation | System and method of dynamic service composition for business process outsourcing |
US7149736B2 (en) | 2003-09-26 | 2006-12-12 | Microsoft Corporation | Maintaining time-sorted aggregation records representing aggregations of values from multiple database records using multiple partitions |
CA2443447A1 (en) | 2003-09-30 | 2005-03-30 | Ibm Canada Limited-Ibm Canada Limitee | System and method for conversion between graph-based representations and structural text-based representations of business processes |
EP1695167A1 (en) | 2003-12-17 | 2006-08-30 | Telecom Italia S.p.A. | Method and apparatus for monitoring operation of processing systems, related network and computer program product therefor |
US20050154628A1 (en) | 2004-01-13 | 2005-07-14 | Illumen, Inc. | Automated management of business performance information |
US7359909B2 (en) | 2004-03-23 | 2008-04-15 | International Business Machines Corporation | Generating an information catalog for a business model |
US20060053120A1 (en) | 2004-09-07 | 2006-03-09 | Hong Kong Applied Science And Technology Research Institute Co., Ltd. | Web service registry and method of operation |
US20060085473A1 (en) | 2004-10-14 | 2006-04-20 | Frederik Thormaehlen | Method and system for business process super-transaction |
US20060122872A1 (en) | 2004-12-06 | 2006-06-08 | Stevens Harold L | Graphical user interface for and method of use for a computer-implemented system and method for booking travel itineraries |
US8700414B2 (en) | 2004-12-29 | 2014-04-15 | Sap Ag | System supported optimization of event resolution |
US20060173668A1 (en) * | 2005-01-10 | 2006-08-03 | Honeywell International, Inc. | Identifying data patterns |
US7743137B2 (en) * | 2005-02-07 | 2010-06-22 | Microsoft Corporation | Automatically targeting notifications about events on a network to appropriate persons |
US7590668B2 (en) | 2005-04-15 | 2009-09-15 | Microsoft Corporation | Pausable backups of file system items |
-
2005
- 2005-05-20 US US11/133,701 patent/US7627544B2/en not_active Expired - Fee Related
-
2006
- 2006-04-13 WO PCT/US2006/013966 patent/WO2006127169A2/en active Application Filing
- 2006-04-13 CN CNA2006800135163A patent/CN101611394A/en active Pending
- 2006-04-13 EP EP06750102A patent/EP1886233A4/en not_active Withdrawn
- 2006-04-13 KR KR1020077024137A patent/KR20080012265A/en not_active Application Discontinuation
- 2006-04-13 JP JP2008512282A patent/JP2008546054A/en active Pending
Non-Patent Citations (1)
Title |
---|
See references of EP1886233A4 * |
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