Automatic service categorisation through machine learning in emergent middleware

The modern environment of mobile, pervasive, evolving services presents a great challenge to traditional solutions for enabling interoperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To overcome this, we apply machine learning to extract high-level functionality information through text categorisation of a system's interface description. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmaking between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project

Diagnosing and measuring incompatibilities between pairs of services

International audienceThis text presents a tool, from its design to its implementation, which detects all behavioural incompatibilities between two service interfaces. Unlike prior work, the proposed solution does not simply check whether two services are incompatible or not, it rather provides detailed diagnosis, including the incompatibilities and for each one the location in the service interfaces where these incompatibilities occur. A measure of similarity between interfaces which considers outputs from the detection algorithm is proposed too. A visual report of the comparison analysis is also provided which pinpoints a set of incompatibilities that cause a behavioural interface not to simulate another one

Case and Activity Identification for Mining Process Models from Middleware

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider

Analysis and Verification of Service Interaction Protocols - A Brief Survey

Modeling and analysis of interactions among services is a crucial issue in Service-Oriented Computing. Composing Web services is a complicated task which requires techniques and tools to verify that the new system will behave correctly. In this paper, we first overview some formal models proposed in the literature to describe services. Second, we give a brief survey of verification techniques that can be used to analyse services and their interaction. Last, we focus on the realizability and conformance of choreographies.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330

ServiceMosaic: Interactive analysis and manipulations of service conversations

In service-oriented computing, a conversation is a sequence of message exchanges between two or more services to achieve a certain goal, for example to order and pay for goods. A business protocol of a service is a specificatio

WWW 2007 / Track: Web Services Session: Orchestration and Choreography Semi-Automated Adaptation of Service Interactions ABSTRACT

In today’s Web, many functionality-wise similar Web services are offered through heterogeneous interfaces (operation definitions) and business protocols (ordering constraints defined on legal operation invocation sequences). The typical approach to enable interoperation in such a heterogeneous setting is through developing adapters. There have been approaches for classifying possible mismatches between service interfaces and business protocols to facilitate adapter development. However, the hard job is that of identifying, given two service specifications, the actual mismatches between their interfaces and business protocols. In this paper we present novel techniques and a tool that provides semi-automated support for identifying and resolution of mismatches between service interfaces and protocols, and for generating adapter specification. We make the followin

Extending process logs with events from supplementary sources

Since organizations typically use more than a single IT system, information about the execution of a process is rarely available in a single event log. More commonly, data is scattered across different locations and unlinked by common case identifiers. We present a method to extend an incomplete main event log with events from supplementary data sources, even though the latter lack references to the cases recorded in the main event log. We establish this correlation by using the controlflow, time, resource, and data perspectives of a process model, as well as alignment diagnostics. We evaluate our approach on a real-life event log and discuss the reliability of the correlation under different circumstances. Our evaluation shows that it is possible to correlate a large portion of the events by using our method

Detecting inconsistencies between process models and textual descriptions

Text-based and model-based process descriptions have their own particular strengths and, as such, appeal to different stakeholders. For this reason, it is not unusual to find within an organization descriptions of the same business processes in both modes. When considering that hundreds of such descriptions may be in use in a particular organization by dozens of people, using a variety of editors, there is a clear risk that such models become misaligned. To reduce the time and effort needed to repair such situations, this paper presents the first approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach leverages natural language processing techniques to identify cases where the two process representations describe activities in different orders, as well as model activities that are missing from the textual description. A quantitative evaluation with 46 real-life model-text pairs demonstrates that our approach allows users to quickly and effectively identify those descriptions in a process repository that are inconsistent