The State-of-the-arts in Focused Search

The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a user’s topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems

Supporting telecom business processes by means of workflow management and federated databases

This report addresses the issues related to the use of workflow management\ud systems and federated databases to support business processes that operate on\ud large and heterogeneous collections of autonomous information systems. We\ud discuss how they can enhance the overall IT-architecture. Starting from the\ud OSCA architecture, we develop an architecture that includes workflow\ud management systems and federated databases. In this architecture, the notion of\ud information systems as a monolithic entity disappears. Instead, business\ud processes are supported directly by workflows that combine presentation\ud blocks, function blocks, and data blocks. We address the specific issues of\ud transaction management and change management in such an architecture

An Identity-Based Group Signature with Membership Revocation in the Standard Model

Group signatures allow group members to sign an arbitrary number\ud of messages on behalf of the group without revealing their\ud identity. Under certain circumstances the group manager holding a\ud tracing key can reveal the identity of the signer from the\ud signature. Practical group signature schemes should support\ud membership revocation where the revoked member loses the\ud capability to sign a message on behalf of the group without\ud influencing the other non-revoked members. A model known as\ud \emph{verifier-local revocation} supports membership revocation.\ud In this model the trusted revocation authority sends revocation\ud messages to the verifiers and there is no need for the trusted\ud revocation authority to contact non-revoked members to update\ud their secret keys. Previous constructions of verifier-local\ud revocation group signature schemes either have a security proof in the\ud random oracle model or are non-identity based. A security proof\ud in the random oracle model is only a heuristic proof and\ud non-identity-based group signature suffer from standard Public Key\ud Infrastructure (PKI) problems, i.e. the group public key is not\ud derived from the group identity and therefore has to be certified.\ud \ud \ud In this work we construct the first verifier-local revocation group\ud signature scheme which is identity-based and which has a security proof in the standard model. In\ud particular, we give a formal security model for the proposed\ud scheme and prove that the scheme has the\ud property of selfless-anonymity under the decision Linear (DLIN)\ud assumption and it is fully-traceable under the\ud Computation Diffie-Hellman (CDH) assumption. The proposed scheme is based on prime order bilinear\ud groups

Inference Optimization using Relational Algebra

Exact inference procedures in Bayesian networks can be expressed using relational algebra; this provides a common ground for optimizations from the AI and database communities. Specifically, the ability to accomodate sparse representations of probability distributions opens up the way to optimize for their cardinality instead of the dimensionality; we apply this in a sensor data model.\u

Query Load Balancing by Caching Search Results in Peer-to-Peer Information Retrieval Networks

For peer-to-peer web search engines it is important to keep the delay between receiving a query and providing search results within an acceptable range for the end user. How to achieve this remains an open challenge. One way to reduce delays is by caching search results for queries and allowing peers to access each others cache. In this paper we explore the limitations of search result caching in large-scale peer-to-peer information retrieval networks by simulating such networks with increasing levels of realism. We find that cache hit ratios of at least thirty-three percent are attainable

Qualitative Effects of Knowledge Rules in Probabilistic Data Integration

One of the problems in data integration is data overlap: the fact that different data sources have data on the same real world entities. Much development time in data integration projects is devoted to entity resolution. Often advanced similarity measurement techniques are used to remove semantic duplicates from the integration result or solve other semantic conflicts, but it proofs impossible to get rid of all semantic problems in data integration. An often-used rule of thumb states that about 90% of the development effort is devoted to solving the remaining 10% hard cases. In an attempt to significantly decrease human effort at data integration time, we have proposed an approach that stores any remaining semantic uncertainty and conflicts in a probabilistic database enabling it to already be meaningfully used. The main development effort in our approach is devoted to defining and tuning knowledge rules and thresholds. Rules and thresholds directly impact the size and quality of the integration result. We measure integration quality indirectly by measuring the quality of answers to queries on the integrated data set in an information retrieval-like way. The main contribution of this report is an experimental investigation of the effects and sensitivity of rule definition and threshold tuning on the integration quality. This proves that our approach indeed reduces development effort — and not merely shifts the effort to rule definition and threshold tuning — by showing that setting rough safe thresholds and defining only a few rules suffices to produce a ‘good enough’ integration that can be meaningfully used

Converting relational databases into object relational databases

This paper proposes an approach for migrating existing Relational DataBases (RDBs) into Object-Relational DataBases (ORDBs). The approach is superior to existing proposals as it can generate not only the target schema but also the data instances. The solution takes an existing RDB as input, enriches its metadata representation with required semantics, and generates an enhanced canonical data model, which captures essential characteristics of the target ORDB, and is suitable for migration. A prototype has been developed, which migrates successfully RDBs into ORDBs (Oracle 11g) based on the canonical model. The experimental results were very encouraging, demonstrating that the proposed approach is feasible, efficient and correct

E-learning as a Vehicle for Knowledge Management

Nowadays, companies want to learn from their own experiences and to be able to enhance that experience with best principles and lessons learned from other companies. Companies emphasise the importance of knowledge management, particularly the relationship between knowledge and learning within an organisation. We feel that an e-learning environment may contribute to knowledge management on the one hand and to the learning need in companies on the other hand. In this paper, we report on the challenges in designing and implementing an e-learning environment. We identify the properties from a pedagogical view that should be supported by an e-learning environment. Then, we discuss the challenges in developing a system that includes these properties

Discovering Process Reference Models from Process Variants Using Clustering Techniques

In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms