Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

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Universal Declaration of Human Rights

https://kodu.ut.ee/~dumas/pubs/BetaWPSimilarity.pdf

Similarity of business process models: Metrics and evaluation

Purpose – In the last two decades, a proliferation of business process management (BPM) modeling languages, standards and software systems has given rise to much confusion and obstacles to adoption. Since new BPM languages and notation terminologies were not well defined, duplicate features are common. This paper seeks to make sense of the myriad BPM standards, organising them in a classification framework, and to identify key industry trends.Design/methodology/approach – An extensive literature review is conducted and relevant BPM notations, languages and standards are referenced against the proposed BPM Standards Classification Framework, which lists each standard's distinct features, strengths and weaknesses.Findings – The paper is unaware of any classification of BPM languages. An attempt is made to classify BPM languages, standards and notations into four main groups: execution, interchange, graphical, and diagnosis standards. At the present time, there is a lack of established diagnosis standards. I...

Business process management (BPM) standards: a survey

A business process may be modeled in different ways by different modelers even when utilizing the same modeling language. An appropriate method for solving ambiguity issues in process models caused by the use of synonyms, homonyms or different abstraction levels for process element names is the use of ontology-based descriptions of process models. So-called semantic business process models promise to support business process interoperability and interconnectivity. But, for (semi-) automatic process interoperability and interconnectivity two problems need to be solved. How can similar terms for process element names be automatically discovered and how can semantic business process composition be facilitated. In this paper we will present solutions for these problems based upon an OWL DL-based description of Petri nets.

/pdf/measuring-similarity-between-semantic-business-process-13ies8waf2.pdf

Measuring similarity between semantic business process models

Social software has received much attention in the academia and industry due to many success stories. However, although social software is used widely for business support, its relationship with Business Process Management has not been analysed. The results of the workshop on Business Process Management and Social Software (BPMS2'08), as part of the International Conference on Business Process Management in Milano, show the manifold possibilities of combining concepts from Business Process Management and social software. Social software provides a better integration of all stakeholders into the business process life cycle and offers new possibilities for a more effective and flexible design of business processes. The modelling of business processes may profit particularly from using social software techniques by alleviating the integration of process knowledge from all stakeholders. In addition, the implementation and deployment phase of the business process life cycle may profit from social software by collecting valuable information for continuous process improvement from a larger set of sources than before. Furthermore, social software environments may be used to provide workflow support. Moreover, the use of social software also requires new considerations about the digital identity and reputation in business processes. Copyright © 2010 John Wiley & Sons, Ltd.

Social software provides a better integration of all stakeholders into the business process lifecycle and offers new possibilities for a more effective and flexible design of business processes. The modelling of business processes may profit particularly from using social software techniques by alleviating the integration of process knowledge from all stakeholders. Also the implementation and deployment phase of the business process lifecycle may profit from social software by collecting valuable information for continuous process improvement from a larger set of sources than before. Furthermore, social software environments may be used to provide workflow support. Furthermore, the use of social software also requires new considerations about digital identity and reputation in business processes. Copyright © 2010 John Wiley & Sons, Ltd.

Combining BPM and social software: contradiction or chance?

To ensure proper and efficient modeling of business processes, it is important to support users of process editors adequately. With only minimal modeling support, the productivity of novice business process modelers may be low when starting process modeling. In this article, we present a theoretically sound and empirically validated recommendation-based modeling support system, which covers different aspects of business process modeling. We consider basic functionality, such as an intuitive search interface, as well as advanced concepts like patterns observed in other users' preferences. Additionally, we propose a multitude of interaction possibilities with the recommendation system, e.g., different metrics that can be used in isolation or an overall recommender component that combines several sub metrics into one comprehensive score. We validate a prototype implementation of the recommendation system with exhaustive user experiments based on real-life process models. To our knowledge, this is the only comprehensive recommendation system for business process modeling that is available.

Recommendation-based editor for business process modeling

Coupling of cross-organizational business processes in electronic markets is a difficult and time-consuming task. In practice business processes are geographically distributed which makes it particulary difficult for business partners to coordinate their supply chains and customer relationship management with business units. By using formal description languages such as Petri nets for modeling inter-organizational business processes, purely syntactic composition problems of distributed business environments can be solved. However, the missing semantic representation of Petri nets can hamper the interconnectivity of business processes. Usually, several business partners, even if they share similar demands, have their own specific vocabularies. By representing business processes with Petri nets in combination with the Web Ontology Language (OWL) our approach provides flexibility, ease of integration and a significant level of automation of loosely coupled business processes even if they do not share their respective vocabularies.

/pdf/ontology-based-business-process-description-hemgpjc6ue.pdf

Ontology Based Business Process Description.

This paper presents a method for semantically aligning business processes. We provide a representation of Petri nets in the ontology language OWL, to semantically enrich the business process models. On top of this, we propose a technique for semantically aligning business processes to support (semi)automatic interconnectivity of business processes. This semantic alignment is improved by a background ontology modeled with a specific UML Profile allowing to visually model it. The different parts of our proposal, which reduces communication efforts and solves interconnectivity problems, are discussed.

/pdf/semantic-alignment-of-business-processes-cvarcufozs.pdf

Agnes Koschmider

Papers

Measuring similarity between semantic business process models

Combining BPM and social software: contradiction or chance?

Recommendation-based editor for business process modeling

Ontology Based Business Process Description.

Semantic alignment of business processes