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

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

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

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

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

Towards Automatic Capturing of Manual Data Processing Provenance

Often data processing is not implemented by a work ow system or an integration application but is performed manually by humans along the lines of a more or less specified procedure. Collecting provenance information during manual data processing can not be automated. Further, manual collection of provenance information is error prone and time consuming. Therefore, we propose to infer provenance information based on the read and write access of users. The derived provenance information is complete, but has a low precision. Therefore, we propose further to introducing organizational guidelines in order to improve the precision of the inferred provenance information

Towards database support for moving object data

To narrow down moving object challenges, the focus of this thesis is on four issues, namely, uncertainty handling for moving object data, faithful trajectory representation, trajectory compression techniques, and similarity measures for trajectories

Graphing of E-Science Data with varying user requirements

Based on our experience in the Swiss Experiment, exploring experimental, scientific data is often done in a visual way. Starting from a global overview the users are zooming in on interesting events. In case of huge data volumes special data structures have to be introduced to provide fast and easy access to the data. Since it is hard to predict on how users will work with the data a generic approach requires self-adaptation of the required special data structures. In this paper we describe the underlying NP-hard problem and present several approaches to address the problem with varying properties. The approaches are illustrated with a small example and are evaluated with a synthetic data set and user queries

Indeterministic Handling of Uncertain Decisions in Duplicate Detection

In current research, duplicate detection is usually considered as a deterministic approach in which tuples are either declared as duplicates or not. However, most often it is not completely clear whether two tuples represent the same real-world entity or not. In deterministic approaches, however, this uncertainty is ignored, which in turn can lead to false decisions. In this paper, we present an indeterministic approach for handling uncertain decisions in a duplicate detection process by using a probabilistic target schema. Thus, instead of deciding between multiple possible worlds, all these worlds can be modeled in the resulting data. This approach minimizes the negative impacts of false decisions. Furthermore, the duplicate detection process becomes almost fully automatic and human effort can be reduced to a large extent. Unfortunately, a full-indeterministic approach is by definition too expensive (in time as well as in storage) and hence impractical. For that reason, we additionally introduce several semi-indeterministic methods for heuristically reducing the set of indeterministic handled decisions in a meaningful way