PROCESSING EVENT INSTANCE DATA IN A CLIENT-SERVER ARCHITECTURE

• US 20130212157A1
• Filed: 03/30/2012
• Published: 08/15/2013
• Est. Priority Date: 02/15/2012
• Status: Abandoned Application

• Current Assignee: QPR Software Oyj
• Original Assignee: QPR Software Oyj
• Inventors: LEHTO, Teemu, HINKKA, Markku
• Application Number:
  US13/435,188
• Publication Number:
  US 20130212157A1
• Time in Patent Office: Days
• US Class Current: 709/203
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/0633 Workflow analysis
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30

Abstract

A process analysis system (1-300) processes event data describing real-world processes (1-100). The process analysis system performs the following acts: importing event instance data sets from an information management system (1-200), each set comprising one or more attributes describing an event instance in the real-world process (1-100); for each event instance, determining a corresponding process instance based on at least the attributes; determining event order attribute(s) for each imported event instance data set based on other event instance data sets corresponding to the same process instance; forming an analysis result set based on at least the event instance data sets and at least one first or second attribute; the client(s) presenting an analysis utilizing the analysis result set.

Claims

1. A method for analyzing information derived from event data by a computer-implemented analysis system, which comprises a server and one or more clients, wherein the event data describes a real-world process the execution of which is supported by at least one information management system but the real-world process is not directly connectable with the computer-implemented analysis system, the method comprising the following acts performed by the server:

importing event instance data comprising a plurality of event instance data sets from the at least one information management system, wherein each event instance data set comprises one or more attributes describing an event instance in the real-world process;

determining for each imported event instance data set a corresponding process instance based on at least the attributes of the imported event instance data set;

determining at least one event order attribute for each imported event instance data set based on other event instance data sets corresponding to the same process instance;

calculating order information for each event instance data set so that for each event instance data set, an unambiguous and unique predecessor event and successor event can be deduced based on the order information and process instance identifier;

determining for each process instance an ordered list of related event instance data sets based on the order information of event instance data sets;

calculating process variation information for each process instance based on at least one attribute of each event in the ordered list of related event instance data sets;

storing the calculated process variation information in the server;

forming an analysis result set based on at least the event instance data sets and the calculated process variation information;

sending the analysis result set to one or more clients; and

wherein the method further comprises;

at the one or more clients, presenting an analysis utilizing the analysis result set.

2. The method according to claim 1, further comprising:

the server storing the calculated process variation information so that for every process instance the calculated process variation information includes at least the ordered list of event type identifiers of the event instance data sets connected to the process instance

3. The method according to claim 2, further comprising:

the server storing the calculated process variation information so that the ordered list of identifiers is stored in a single database attribute for the process variation so that it can be referred with database functions.

4. The method according to claim 2, further comprising:

the server calculating and storing a hash-like variable calculated based on the process variation so that for each new process instance the server can effectively search and identify an already existing process variation or create a new variation.

5. The method according to claim 2, further comprising:

the server using previously stored variation information when calculating new variation information for a new analysis based on a new set of event instance data sets.

6. The method according to claim 2, further comprising:

the server creating and storing a view for each filtered set of the event instance data sets, wherein the stored view includes at least information indicating which event instance data sets are included or excluded from the analysis; and

the server storing the variation information separately for each view.

8. The computer-implemented analysis system according to claim 2, wherein the server comprises multiple processing units and a load-balancing unit for distributing processing load among the multiple processing units.

7. A computer-implemented analysis system comprising a server for supporting one or more clients, the server comprising:

at least one processing unit

memory for storing applications and data;

wherein the memory comprises program code instructions for instructing the at least one processing unit to carry out the following steps;

importing event instance data comprising a plurality of event instance data sets from the at least one information management system, wherein each event instance data set comprises one or more attributes describing an event instance in the real-world process;

determining for each imported event instance data set a corresponding process instance based on at least the attributes of the imported event instance data set;

determining at least one event order attribute for each imported event instance data set based on other event instance data sets corresponding to the same process instance;

calculating order information for each event instance data set so that for each event instance data set, an unambiguous and unique predecessor and successor event can be deduced based on the order information and process instance identifier;

determining for each process instance an ordered list of related event instance data sets based on the order information of event instance data sets;

calculating process variation information for each process instance based on at least one attribute of each event in the ordered list of related event instance data sets;

storing the calculated process variation information in the server;

forming an analysis result set based on at least the event instance data sets and the calculated process variation information;

sending the analysis result set to one or more clients.

9. A computer-readable memory comprising program code instructions for a server of a process analysis system that also comprises one or more clients, wherein the program code instructions, when executed by the server, cause the server to perform the steps of:

importing event instance data comprising a plurality of event instance data sets from the at least one information management system, wherein each event instance data set comprises one or more attributes describing an event instance in the real-world process;

determining for each imported event instance data set a corresponding process instance based on at least the attributes of the imported event instance data set;

determining at least one event order attribute for each imported event instance data set based on other event instance data sets corresponding to the same process instance;

calculating order information for each event instance data set so that for each event instance data set, an unambiguous and unique predecessor and successor event can be deduced based on the order information and process instance identifier;

determining for each process instance an ordered list of related event instance data sets based on the order information of event instance data sets;

calculating process variation information for each process instance based on at least one attribute of each event in the ordered list of related event instance data sets;

storing the calculated process variation information in the server;

forming an analysis result set based on at least the event instance data sets and the calculated process variation information;

sending the analysis result set to one or more clients.