Avigdor Gal

Bio: Avigdor Gal is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Schema matching & Matching (statistics). The author has an hindex of 35, co-authored 252 publications receiving 4861 citations. Previous affiliations of Avigdor Gal include Rutgers University & German Research Centre for Artificial Intelligence.

Blockchains for Business Process Management - Challenges and Opportunities

Abstract: Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

On the move to meaningful Internet systems 2004 : CoopIS, DOA, and ODBASE : OTM confederated international conferences, CoopIS, DOA, and ODBASE 2004, Agia Napa, Cyprus, October 25-29, 2004 : proceedings (part II)

Abstract: Cooperative Information Systems (CoopIS) 2004 International Conference- CoopIS 2004 International Conference (International Conference on Cooperative Information Systems) PC Co-chairs' Message- Keynote- Business Process Optimization- Workflow/Process/Web Services, I- Discovering Workflow Transactional Behavior from Event-Based Log- A Flexible Mediation Process for Large Distributed Information Systems- Exception Handling Through a Workflow- Workflow/Process/Web Services, II- A Flexible and Composite Schema Matching Algorithm- Analysis, Transformation, and Improvements of ebXML Choreographies Based on Workflow Patterns- The Notion of Business Process Revisited- Workflow/Process/Web Services, III- Disjoint and Overlapping Process Changes: Challenges, Solutions, Applications- Untangling Unstructured Cyclic Flows - A Solution Based on Continuations- Making Workflow Models Sound Using Petri Net Controller Synthesis- Database Management/Transaction- Concurrent Undo Operations in Collaborative Environments Using Operational Transformation- Refresco: Improving Query Performance Through Freshness Control in a Database Cluster- Automated Supervision of Data Production - Managing the Creation of Statistical Reports on Periodic Data- Schema Integration/Agents- Deriving Sub-schema Similarities from Semantically Heterogeneous XML Sources- Supporting Similarity Operations Based on Approximate String Matching on the Web- Managing Semantic Compensation in a Multi-agent System- Modelling with Ubiquitous Agents a Web-Based Information System Accessed Through Mobile Devices- Events- A Meta-service for Event Notification- Classification and Analysis of Distributed Event Filtering Algorithms- P2P/Collaboration- A Collaborative Model for Agricultural Supply Chains- FairNet - How to Counter Free Riding in Peer-to-Peer Data Structures- Supporting Collaborative Layouting in Word Processing- A Reliable Content-Based Routing Protocol over Structured Peer-to-Peer Networks- Applications, I- Covering Your Back: Intelligent Virtual Agents in Humanitarian Missions Providing Mutual Support- Dynamic Modelling of Demand Driven Value Networks- An E-marketplace for Auctions and Negotiations in the Constructions Sector- Applications, II- Managing Changes to Engineering Products Through the Co-ordination of Human and Technical Activities- Towards Automatic Deployment in eHome Systems: Description Language and Tool Support- A Prototype of a Context-Based Architecture for Intelligent Home Environments- Trust/Security/Contracts- Trust-Aware Collaborative Filtering for Recommender Systems- Service Graphs for Building Trust- Detecting Violators of Multi-party Contracts- Potpourri- Leadership Maintenance in Group-Based Location Management Scheme- TLS: A Tree-Based DHT Lookup Service for Highly Dynamic Networks- Minimizing the Network Distance in Distributed Web Crawling- Ontologies, DataBases, and Applications of Semantics (ODBASE) 2004 International Conference- ODBASE 2004 International Conference (Ontologies, DataBases, and Applications of Semantics) PC Co-chairs' Message- Keynote- Helping People (and Machines) Understanding Each Other: The Role of Formal Ontology- Knowledge Extraction- Automatic Initiation of an Ontology- Knowledge Extraction from Classification Schemas- Semantic Web in Practice- Generation and Management of a Medical Ontology in a Semantic Web Retrieval System- Semantic Web Based Content Enrichment and Knowledge Reuse in E-science- The Role of Foundational Ontologies in Manufacturing Domain Applications- Intellectual Property Rights Management Using a Semantic Web Information System- Ontologies and IR- Intelligent Retrieval of Digital Resources by Exploiting Their Semantic Context- The Chrysostom Knowledge Base: An Ontology of Historical Interactions- Text Simplification for Information-Seeking Applications- Information Integration- Integration of Integrity Constraints in Federated Schemata Based on Tight Constraining- Modal Query Language for Databases with Partial Orders- Composing Mappings Between Schemas Using a Reference Ontology- Assisting Ontology Integration with Existing Thesauri

Comparative analysis of approximate blocking techniques for entity resolution

Abstract: Entity Resolution is a core task for merging data collections. Due to its quadratic complexity, it typically scales to large volumes of data through blocking: similar entities are clustered into blocks and pair-wise comparisons are executed only between co-occurring entities, at the cost of some missed matches. There are numerous blocking methods, and the aim of this work is to offer a comprehensive empirical survey, extending the dimensions of comparison beyond what is commonly available in the literature. We consider 17 state-of-the-art blocking methods and use 6 popular real datasets to examine the robustness of their internal configurations and their relative balance between effectiveness and time efficiency. We also investigate their scalability over a corpus of 7 established synthetic datasets that range from 10,000 to 2 million entities.

A framework for modeling and evaluating automatic semantic reconciliation

Abstract: The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.

Blockchains for Business Process Management - Challenges and Opportunities

Abstract: Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.

Machine learning

Abstract: 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.).

Ontology Matching

Abstract: Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Blockchain technology and its relationships to sustainable supply chain management

Abstract: Globalisation of supply chains makes their management and control more difficult. Blockchain technology, as a distributed digital ledger technology which ensures transparency, traceability, and sec...

Universal Declaration of Human Rights

Abstract: It made it possible to improve people's lives. Now it prevents all forms of discrimination in the world. It helps to improve our world.