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

Business Process Modeling Abstraction Based on Semi-Supervised Clustering Analysis

The most prominent Business Process Model Abstraction (BPMA) use case is the construction of the process “quick view” for rapidly comprehending a complex process. Some researchers propose process abstraction methods to aggregate the activities on the basis of their semantic similarity. One important clustering technique used in these methods is traditional k-means cluster analysis which so far is an unsupervised process without any priori information, and most of the techniques aggregate the activities only according to business semantics without considering the requirement of an order-preserving model transformation. The paper proposes a BPMA method based on semi-supervised clustering which chooses the initial clusters based on the refined process structure tree and designs constraints by combining the control flow consistency of the process and the semantic similarity of the activities to guide the clustering process. To be more precise, the constraint function is discovered by mining from a process model collection enriched with subprocess relations. The proposed method is validated by applying it to a process model repository in use. In an experimental validation, the proposed method is compared to the traditional k-means clustering (parameterized with randomly chosen initial clusters and an only semantics-based distance measure), showing that the approach closely approximates the decisions of the involved modelers to cluster activities. As such, the paper contributes to the development of modeling support for effective process model abstraction, facilitating the use of business process models in practice

Business Process Retrieval Based on Behavioral Semantics

This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie

Pengukuran Kemiripan Proses Bisnis Menggunakan Probabilistic Latent Semantic Analysis (PLSA) Dan Greedy Graph Matching

Proses bisnis merupakan serangkaian aktivitas dan tugas yang dilakukan untuk mencapai tujuan dari sebuah organisasi. Proses bisnis dapat dikatakan sebagai alat atau instrumen untuk mengatur keseluruhan kegiatan. Model proses bisnis dapat digunakan kembali sebagai upaya manajemen proses bisnis tersebut ke dalam sebuah repositori. Untuk mengatasi permasalahan tersebut, maka dibutuhkan pengukuran terhadap model proses bisnis yang memiliki kesamaan atau kemiripan dalam hal aktivitas ataupun prosesnya. Dari beberapa model proses bisnis yang memiliki kemiripan (similarity) dapat diidentifikasi sebagai model proses bisnis utama, yang memiliki keutamaan fungsi, hingga aktivitas yang sama. Mencocokkan model proses bisnis menjadi salah satu teknik yang dapat digunakan untuk mengidentifikasi, mengukur kemiripan dari kumpulan model proses bisnis. Dalam bahasa pemodelan secara umum, BPM dimodelkan dalam bentuk graf. Graf ini merepresentasikan alur 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 menghitung kemiripan struktur bangunan graf. Pendekatan lain dalam pencocokkan graf ialah secara semantik atau berbasis teks. Probabilistic Latent Semantic Analysis (PLSA) merupakan salah satu pendekatan semantik untuk menghitung kemiripan teks dalam dokumen. Secara semantik, PLSA menghitung keterkaitan kata dalam dokumen untuk mengidentifikasi adanya kemiripan topik dalam dokumen. Perhitungan PLSA dalam analisis pencocokkan proses bisnis adalah dengan membandingkan label teks pada tiap node di bisnis proses. Dari gabungan dua teknik PLSA dan Greedy Graph Matching menghasilkan nilai presisi sebesar 86%. 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 tiap-tiap label pada aktivitas yang memiliki keterkaitan dengan label aktivitas lain. Secara struktural, aktivitas-aktivitas yang terhubung memiliki keterkaitan proses atau fungsi yang sama. Hasil dari penelitian ini adalah untuk mengetahui efektifitas dari proses bisnis yang memiliki keterkaitan aktivitas. ================================================================================================================== The business process is a set of activities and tasks performed to achieve the goals of an organization. A business process can be regarded as a tool or instrument to manage the whole activity. 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. In general modelling language, BPM is modelled in a graph. This graph represents the business process flow. 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 calculates the similarity of graph 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 calculate the similarity of text in documents. Semantically, the PLSA calculates the linkage of words in the document to identify any similarity of topics in the document. The calculation of PLSA in business process matching analysis is by comparing text labels on each node in the business process. From the combination of two techniques of PLSA and Greedy Graph Matching yields a precision value of 86%. 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

Piloting an Empirical Study on Measures for Workflow Similarity

Service discovery of state dependent services has to take workflow aspects into account. To increase the usability of a service discovery, the result list of services should be ordered with regard to the relevance of the services. Means of ordering a list of workflows due to their similarity with regard to a query are missing. This paper presents a pilot of an empirical study on the influence of different measures on workflow similarity. It turns out that, although preliminary, relations between different measures are indicated and that a similarity definition depends on the application scenario in which the service discovery is applied

APQL: A process-model query language

As business process management technology matures, organisations acquire more and more business process models. The management of the resulting collections of process models poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks in process models and is independent of any particular process modelling notation

A document management methodology based on similarity contents

The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain

Semantic-driven matchmaking of web services using case-based reasoning

With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015