Bridging the Gap Between Business Process Models and Service Composition Specifications

Fundamental goals of any Service Oriented Architecture (SOA) include the flexible support and adaptability of business processes as well as improved business-IT alignment. Existing approaches, however, have failed to fully meet these goals. One of the major reasons for this deficiency is the gap that exists between business process models on the one hand and workflow specifications and implementations (e.g., service composition schemes) on the other hand. In practice, each of these two perspectives has to be regarded separately. In addition, even simple changes to one perspective (e.g. due to new regulations or organizational change) require error-prone, manual re-editing of the other one. Over time, this leads to degeneration and divergence of the respective models and specifications. This aggravates maintenance and makes expensive refactoring inevitable. This chapter presents a flexible approach for aligning business process models with workflow specifications. In order to maintain the complex dependencies that exist between high-level business process models (as used by domain experts) and technical workflow specifications (i.e., service composition schemas), respectively, (as used in IT departments) we introduce an additional model layer – the so-called system model. Furthermore, we explicitly document the mappings between the different levels (e.g., between business process model and system model). This simplifies model adoptions by orders of magnitudes when compared to existing approaches

Äriprotsessimudelite ühildamine

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Ettevõtted, kellel on aastatepikkune kogemus äriprotsesside haldamises, omavad sageli protsesside repositooriumeid, mis võivad endas sisaldada sadu või isegi tuhandeid äriprotsessimudeleid. Need mudelid pärinevad erinevatest allikatest ja need on loonud ning neid on muutnud erinevad osapooled, kellel on erinevad modelleerimise oskused ning praktikad. üheks sagedaseks praktikaks on uute mudelite loomine, kasutades olemasolevaid mudeleid, kopeerides neist fragmente ning neid seejärel muutes. See omakorda loob olukorra, kus protsessimudelite repositoorium sisaldab mudeleid, milles on identseid mudeli fragmente, mis viitavad samale alamprotsessile. Kui sellised fragmendid jätta konsolideerimata, siis võib see põhjustada repositooriumis ebakõlasid -- üks ja sama alamprotsess võib olla erinevates protsessides erinevalt kirjeldatud. Sageli on ettevõtetel mudelid, millel on sarnased eesmärgid, kuid mis on mõeldud erinevate klientide, toodete, äriüksuste või geograafiliste regioonide jaoks. Näiteks on äriprotsessid kodukindlustuse ja autokindlustuse jaoks sama ärilise eesmärgiga. Loomulikult sisaldavad nende protsesside mudelid mitmeid identseid alamfragmente (nagu näiteks poliisi andmete kontrollimine), samas on need protsessid mitmes punktis erinevad. Nende protsesside eraldi haldamine on ebaefektiivne ning tekitab liiasusi. Doktoritöös otsisime vastust küsimusele: kuidas identifitseerida protsessimudelite repositooriumis korduvaid mudelite fragmente, ning üldisemalt -- kuidas leida ning konsolideerida sarnasusi suurtes äriprotsessimudelite repositooriumites? Doktoritöös on sisse toodud kaks üksteist täiendavat meetodit äriprotsessimudelite konsolideerimiseks, täpsemalt protsessimudelite ühildamine üheks mudeliks ning mudelifragmentide ekstraktimine. Esimene neist võtab sisendiks kaks või enam protsessimudelit ning konstrueerib neist ühe konsolideeritud protsessimudeli, mis sisaldab kõikide sisendmudelite käitumist. Selline lähenemine võimaldab analüütikutel hallata korraga tervet perekonda sarnaseid mudeleid ning neid muuta sünkroniseeritud viisil. Teine lähenemine, alamprotsesside ekstraktimine, sisaldab endas sagedasti esinevate fragmentide identifitseerimist (protsessimudelites kloonide leidmist) ning nende kapseldamist alamprotsessideks

Process model comparison based on cophenetic distance

The automated comparison of process models has received increasing attention in the last decade, due to the growing existence of process models and repositories, and the consequent need to assess similarities between the underlying processes. Current techniques for process model comparison are either structural (based on graph edit distances), or behavioural (through activity profiles or the analysis of the execution semantics). Accordingly, there is a gap between the quality of the information provided by these two families, i.e., structural techniques may be fast but inaccurate, whilst behavioural are accurate but complex. In this paper we present a novel technique, that is based on a well-known technique to compare labeled trees through the notion of Cophenetic distance. The technique lays between the two families of methods for comparing a process model: it has an structural nature, but can provide accurate information on the differences/similarities of two process models. The experimental evaluation on various benchmarks sets are reported, that position the proposed technique as a valuable tool for process model comparison.Peer ReviewedPostprint (author's final draft

MEASURING BUSINESS PROCESS SIMILARITY USING PROBABILISTIC LATENT SEMANTIC ANALYSIS (PLSA) AND GREEDY GRAPH MATCHING

AbstractThe business process is a set of activities and tasks performed to achieve the goals of an organization. The business process model can be reused as a business process management effort into a repository. To solve the problem, it is necessary to measure the business process model that has similarity or similarity in terms of activity or process. From several business process models that have similarity can be identified as the main business process model, which has the primary function of the same activity. Business process model matching is the one of technique that can be used to identify, to measure the similarity of a set of business process models. The graph matching approach fit to identify the similarity of processes or activities in the business process model. The technique of matching the graph with Greedy graph matching shows similar results with an 89% precision value based on measuring the similarity of the graph building structure. Another approach in graph matching is a semantically or a text-based. Probabilistic Latent Semantic Analysis (PLSA) is one of the semantic approaches to measure the similarity of text in documents. PLSA measures the linkage of words in the document to identify any similarity of topics in the document. Measuring PLSA in business process matching analysis is by comparing text labels on each node in the business process. This research measures the similarity of business process models by combining two similarity analysis techniques based on semantics using PLSA and structural with Greedy. A graph matching technique by computing the semantics of each label on activities that are related to other activity labels. Structurally, connected activities are related to the same process or the same function. The result of this research is to know the effectiveness of business process which has activity relation.Keywords : Business Process, BPMN, Graph Similarity, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph MatchingProses bisnis adalah serangkaian aktivitas dan tugas yang dilakukan untuk mencapai tujuan dari sebuah organisasi. Model proses bisnis dapat digunakan kembali sebagai upaya manajemen proses bisnis tersebut ke dalam sebuah repositori. Dalam repositori berisi ratusan hingga ribuan model proses bisnis dengan model yang sama maupun berbeda. Hingga dapat terjadinya duplikasi dan penumpukkan data. Untuk mengatasi permasalahan tersebut, perlunya dilakukan pengukuran terhadap model proses bisnis yang memiliki kesamaan atau kemiripan dalam hal aktivitas ataupun proses. Beberapa model proses bisnis yang memiliki kemiripan (similarity) dapat diidentifikasi sebagai model proses bisnis utama, yaitu memiliki fungsi dan aktivitas yang sama. Mencocokkan model proses bisnis merupakan salah satu teknik untuk mengidentifikasi, mengukur kemiripan dari kumpulan model proses bisnis. Pendekatan pencocokkan graf (graph matching) cocok untuk mengidentifikasi kemiripan proses atau aktivitas dalam model proses bisnis. Teknik mencocokkan graf dengan Greedy graph matching menghasilkan nilai presisi sebesar 89% berdasarkan pengukuran kemiripan struktur graf. Pendekatan lain dalam pencocokkan graf ialah secara semantik atau teks. Probabilistic Latent Semantic Analysis (PLSA) merupakan salah satu pendekatan semantik untuk menghitung kemiripan teks dalam dokumen. Perhitungan PLSA dalam analisis pencocokkan proses bisnis adalah dengan membandingkan label teks pada tiap node (label) proses bisnis. Penelitian ini mengukur kemiripan model proses bisnis dengan menggabungkan dua teknik analisis kemiripan berdasarkan semantik menggunakan PLSA dan struktural dengan Greedy. Teknik pencocokkan graf dengan menghitung semantik dari setiap label aktivitas yang saling memiliki keterkaitan atau hubungan. Secara struktural, beberapa aktivitas saling terhubung memiliki keterkaitan proses atau fungsi yang sama. Hasil penelitian ini adalah untuk mengetahui efektifitas dari proses bisnis yang memiliki keterkaitan aktivitas.Kata Kunci : Proses Bisnis, BPMN, Kemiripan Graf, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph Matching

MEASURING BUSINESS PROCESS SIMILARITY USING PROBABILISTIC LATENT SEMANTIC ANALYSIS (PLSA) AND GREEDY GRAPH MATCHING

AbstractThe business process is a set of activities and tasks performed to achieve the goals of an organization. The business process model can be reused as a business process management effort into a repository. To solve the problem, it is necessary to measure the business process model that has similarity or similarity in terms of activity or process. From several business process models that have similarity can be identified as the main business process model, which has the primary function of the same activity. Business process model matching is the one of technique that can be used to identify, to measure the similarity of a set of business process models. The graph matching approach fit to identify the similarity of processes or activities in the business process model. The technique of matching the graph with Greedy graph matching shows similar results with an 89% precision value based on measuring the similarity of the graph building structure. Another approach in graph matching is a semantically or a text-based. Probabilistic Latent Semantic Analysis (PLSA) is one of the semantic approaches to measure the similarity of text in documents. PLSA measures the linkage of words in the document to identify any similarity of topics in the document. Measuring PLSA in business process matching analysis is by comparing text labels on each node in the business process. This research measures the similarity of business process models by combining two similarity analysis techniques based on semantics using PLSA and structural with Greedy. A graph matching technique by computing the semantics of each label on activities that are related to other activity labels. Structurally, connected activities are related to the same process or the same function. The result of this research is to know the effectiveness of business process which has activity relation.Keywords : Business Process, BPMN, Graph Similarity, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph MatchingProses bisnis adalah serangkaian aktivitas dan tugas yang dilakukan untuk mencapai tujuan dari sebuah organisasi. Model proses bisnis dapat digunakan kembali sebagai upaya manajemen proses bisnis tersebut ke dalam sebuah repositori. Dalam repositori berisi ratusan hingga ribuan model proses bisnis dengan model yang sama maupun berbeda. Hingga dapat terjadinya duplikasi dan penumpukkan data. Untuk mengatasi permasalahan tersebut, perlunya dilakukan pengukuran terhadap model proses bisnis yang memiliki kesamaan atau kemiripan dalam hal aktivitas ataupun proses. Beberapa model proses bisnis yang memiliki kemiripan (similarity) dapat diidentifikasi sebagai model proses bisnis utama, yaitu memiliki fungsi dan aktivitas yang sama. Mencocokkan model proses bisnis merupakan salah satu teknik untuk mengidentifikasi, mengukur kemiripan dari kumpulan model proses bisnis. Pendekatan pencocokkan graf (graph matching) cocok untuk mengidentifikasi kemiripan proses atau aktivitas dalam model proses bisnis. Teknik mencocokkan graf dengan Greedy graph matching menghasilkan nilai presisi sebesar 89% berdasarkan pengukuran kemiripan struktur graf. Pendekatan lain dalam pencocokkan graf ialah secara semantik atau teks. Probabilistic Latent Semantic Analysis (PLSA) merupakan salah satu pendekatan semantik untuk menghitung kemiripan teks dalam dokumen. Perhitungan PLSA dalam analisis pencocokkan proses bisnis adalah dengan membandingkan label teks pada tiap node (label) proses bisnis. Penelitian ini mengukur kemiripan model proses bisnis dengan menggabungkan dua teknik analisis kemiripan berdasarkan semantik menggunakan PLSA dan struktural dengan Greedy. Teknik pencocokkan graf dengan menghitung semantik dari setiap label aktivitas yang saling memiliki keterkaitan atau hubungan. Secara struktural, beberapa aktivitas saling terhubung memiliki keterkaitan proses atau fungsi yang sama. Hasil penelitian ini adalah untuk mengetahui efektifitas dari proses bisnis yang memiliki keterkaitan aktivitas.Kata Kunci : Proses Bisnis, BPMN, Kemiripan Graf, Probabilistic Latent Semantic Analysis (PLSA), Greedy Graph Matching

Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects

Encoding conformance checking artefacts in SAT

Conformance checking strongly relies on the computation of artefacts, which enable reasoning on the relation between observed and modeled behavior. This paper shows how important conformance artefacts like alignments, anti-alignments or even multi-alignments, defined over the edit distance, can be computed by encoding the problem as a SAT instance. From a general perspective, the work advocates for a unified family of techniques that can compute conformance artefacts in the same way. The prototype implementation of the techniques presented in this paper show capabilities for dealing with some of the current benchmarks, and potential for the near future when optimizations similar to the ones in the literature are incorporated.Peer ReviewedPostprint (author's final draft

The role of stakeholder understanding in aligning IT with business objectives

This article reports a study of senior management experience and their opinions on the issues of effective stakeholder communication and the evolving understanding between business and IT. In particular, we explore the impact of modern business context and practices, the issues of trust, nomenclature and the main barriers to the mutual stakeholder understanding. We find that a lack of communication and a lack of understanding between stakeholders impacts negatively on good alignment as manifested by scope creep, the desire to outsource and a lack of trus