The amount of information, engineers in the automotive domain are confronted with, makes it a challenging task for them to identify and handle the exact information needed during daily work [17, 248]. When reviewing product requirements, for example, engi- neers not only must consider e-mails and office files, but also guidelines and manuals [2]. This information may be accessed through shared drives or enterprise portals.
However, engineers are not only interested in quickly accessing needed information, but additionally require complete, up-to-date and aggregated information, e.g., when conducting a review. iGraph provides an approach that may relieve automotive engineers from this task. Its overall goal is to deliver needed documents (cf. Figure 8.4A) as well as to discover semantically related ones (cf. Figure 8.4B).
8.2.1 Scenario
We consider a scenario dealing with the review of product requirements, which have been documented as functional specifications. The goal is to both improve and approve the respective specifications. Furthermore, the review process is knowledge-intensive comprising large amounts of process information, user interactions (e.g., “perform review meeting”), and involving decision making (e.g., shall the document be approved or not?). Three roles are involved in the process: The author provides the specification to be reviewed. The review moderator organizes the review meetings. Finally, the reviewer analyzes the provided specification and records errors, ambiguities and uncertainties [3]. 8.2.2 Implementation
We realized iGraph6 as a web-based Java application based on the semantic middleware iQser GIN Server 2.0 [171], the web framework Play 2.1.1, the web engine Bootstrap 2.3.1, the JavaScript library jQuery 1.8.3, the database MySQL 5, the text search engine library Lucene 2.4, the JavaScript library D3 3.1.1, HTML5, and CSS3.
Features. First, iGraph enables the comprehensive integration of process information and business processes from heterogeneous data sources. Second, it allows for the syn- tactic and semantic analysis of integrated information and process objects. Finally, it enables the process-oriented delivery of needed process information and business pro- cesses to knowledge workers and decision makers when conducting a review.
In particular, iGraph addresses information- and process-awareness; i.e., it integrates processes (e.g., review process) and related process information (e.g., reviews, templates).
Architecture. iGraph implements all architectural layers of POIL except the context layer; i.e., (1) data layer, (2) semantic layer, and (3) application layer.
The data layer concerns the set of data sources to be integrated. For each data source, a ContentProvider is implemented. Its main task is to transform proprietary process information or business processes into uniform information and process objects. In turn, the semantic layer is responsible for the syntactic and semantic analysis of information and process objects. For this purpose, we apply the semantic middleware iQser GIN Server and implement several AnalyzerTasks. The goal is to classify and group correlated objects (e.g., filled-out review templates). Finally, user behavior is investigated, e.g., the frequency of using particular information in the context of specific process tasks. Finally, the application layer deals with the delivery of process information.
8.2.3 Scenario Support
In the following we show how iGraph can be applied to the sketched scenario. More precisely, we consider one process schema7, three process instances8, and about 300 doc-
6A screencast presenting the iGraph application is available at http://nipro.hs-weingarten.de/screencast. 7
The process schema is modeled with Signavio Process Editor [249].
uments9 (i.e., process information) such as reviews, review templates, manuals, minutes, presentations, and guidelines. Particularly, we pick up a specific task of the review process: the preparation of a functional specification for a review.
The author of the specification wants to identify relevant information objects sup- porting the review preparation. For this purpose, iGraph provides a search box. The reviewer enters a query (e.g., “review template”) into the search box and executes it. Search results are then listed in a table-based view (cf. Figure 8.5). Thereby, each row corresponds to a search result (i.e., an information object), whereas each column con- tains detailed metadata about the information objects from the result set, e.g., its type, author, title, rating, or number of semantically related documents.
Figure 8.5: iGraph: table-based view.
To identify related information objects (e.g., objects addressing the same topic), iGraph provides a graph-based view (cf. Figure 8.6). The latter displays both related information and process objects starting from a specific information object (e.g., a tem- plate). Then, the user can freely navigate through the related documents in the SIN.
In order to quickly identify relevant objects in the SIN, iGraph provides two funda- mental key indicators: (1) link popularity and (2) rate popularity (cf. Section 5.5).
8.2.4 Related Work
Besides iGraph, there exist other applications enabling IL, e.g., in fields like wearable computing [114], weather forecast [115], and healthcare [99]. Moreover, a large num-
Figure 8.6: iGraph: graph-based view.
ber of tools and solutions have been introduced to discover and visualize related docu- ments. For example, enterprise search engines, such as Solr [251], Elasticsearch [252] and OpenSearchServer [253] allow discovering related documents. In turn, Datameer [254] and Lumify [255] deal with the visualization of relationships between information.10 Note that none of these approaches includes a syntactic and semantic analysis of busi- ness processes and the alignment of the latter with respective process information.
Altogether, iGraph applies semantic technology to enable the integration, analysis and delivery of process information to process participants. The table- and graph-based views as well as the use of key indicators (i.e., link popularity and rate popularity) make it easy to identify relevant process information during business process execution.