On Managing Process Variants as an Information Resource

Many business solutions provide best practice process templates, both generic as well as for specific industry sectors. However, it is often the variance from template solutions that provide organizations with intellectual capital and competitive differentiation. Although variance must comply with various contractual, regulatory and operational constraints, it is still an important information resource, representing preferred work practices. In this paper, we present a modeling framework that is conducive to constrained variance, by supporting user driven process adaptations. The focus of the paper is on providing a means of utilizing the adaptations effectively for process improvement through effective management of the process variants repository (PVR). In particular, we will provide deliberations towards a facility to provide query functionality for PVR that is specifically targeted for effective search and retrieval of process variants

An Integrated Content and Metadata based Retrieval System for Art

In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type

Towards Case Completion with inferencing and solution identification using ‘Nested CBR’

Case Based Reasoning (CBR) provides a framework to capture past problems and their solutions to solve future problems. Problem cases are typically complete; however, it is not always possible to have a complete problem case due to complexity, lack of data, or availability of human expertise. The limitations of existing approaches for handling incomplete cases include a reliance upon manual input, such as Conversational CBR (CCBR) and Incremental CBR (ICBR), or a rigid structure of relationships maintained using a semantic ontology, to infer the missing feature values. Using the case base to infer feature values increases the efficiency and likelihood of identifying a relevant solution compared with manual interactions because the case base is based upon proven problem to solution correlation. Therefore, in this work-in-progress paper, we propose \u27Nested CBR\u27 as an approach for the automated completion of partial problem cases, and the subsequent solution identification, thereby avoiding manual input and improving solution efficiency and meaning

A fuzzy approach to similarity in Case-Based Reasoning suitable to SQL implementation

The aim of this paper is to formally introduce a notion of acceptance and similarity, based on fuzzy logic, among case features in a case retrieval system. This is pursued by rst reviewing the relationships between distance-based similarity (i.e. the standard approach in CBR) and fuzzy-based similarity, with particular attention to the formalization of a case retrieval process based on fuzzy query specication. In particular, we present an approach where local acceptance relative to a feature can be expressed through fuzzy distributions on its domain, abstracting the actual values to linguistic terms. Furthermore, global acceptance is completely grounded on fuzzy logic, by means of the usual combinations of local distributions through specic dened norms. We propose a retrieval architecture, based on the above notions and realized through a fuzzy extension of SQL, directly implemented on a standard relational DBMS. The advantage of this approach is that the whole power of an SQL engine can be fully exploited, with no need of implementing specic retrieval algorithms. The approach is illustrated by means of some examples from a recommender system called MyWine, aimed at recommending the suitable wine bottles to a customer providing her requirements in both crisp and fuzzy way

An Efficient Transport Protocol for delivery of Multimedia An Efficient Transport Protocol for delivery of Multimedia Content in Wireless Grids

A grid computing system is designed for solving complicated scientific and commercial problems effectively,whereas mobile computing is a traditional distributed system having computing capability with mobility and adopting wireless communications. Media and Entertainment fields can take advantage from both paradigms by applying its usage in gaming applications and multimedia data management. Multimedia data has to be stored and retrieved in an efficient and effective manner to put it in use. In this paper, we proposed an application layer protocol for delivery of multimedia data in wireless girds i.e. multimedia grid protocol (MMGP). To make streaming efficient a new video compression algorithm called dWave is designed and embedded in the proposed protocol. This protocol will provide faster, reliable access and render an imperceptible QoS in delivering multimedia in wireless grid environment and tackles the challenging issues such as i) intermittent connectivity, ii) device heterogeneity, iii) weak security and iv) device mobility.Comment: 20 pages, 15 figures, Peer Reviewed Journa

Extracting knowledge from web communities and linked data for case-based reasoning systems

Web communities and the Web 2.0 provide a huge amount of experiences and there has been a growing availability of Linked Open Data. Making experiences and data available as knowledge to be used in case-based reasoning CBR systems is a current research effort. The process of extracting such knowledge from the diverse data types used in web communities, to transform data obtained from Linked Data sources, and then formalising it for CBR, is not an easy task. In this paper, we present a prototype, the Knowledge Extraction Workbench KEWo, which supports the knowledge engineer in this task. We integrated the KEWo into the open-source case-based reasoning tool myCBR Workbench. We provide details on the abilities of the KEWo to extract vocabularies from Linked Data sources and generate taxonomies from Linked Data as well as from web community data in the form of semi-structured texts

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable