Graph traversals are a basic but fundamental ingredient for a variety of graph algorithms and graph-oriented queries. To achieve the best possible query performance, they need to be implemented at the core of a database management system that aims at storing, manipulating, and querying graph data. Increasingly, modern business applications demand native graph query and processing capabilities for enterprise-critical operations on data stored in relational database management systems. In this paper we propose an extensible graph traversal framework (GRAPHITE) as a central graph processing component on a common storage engine inside a relational database management system.

Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users' preferences and significantly support the users' understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph.

The SAP HANA database is the core of SAP's new data management platform. The overall goal of the SAP HANA database is to provide a generic but powerful system for different query scenarios, both transactional and analytical, on the same data representation within a highly scalable execution environment. Within this paper, we highlight the main features that differentiate the SAP HANA database from classical relational database engines. Therefore, we outline the general architecture and design criteria of the SAP HANA in a first step. In a second step, we challenge the common belief that column store data structures are only superior in analytical workloads and not well suited for transactional workloads. We outline the concept of record life cycle management to use different storage formats for the different stages of a record. We not only discuss the general concept but also dive into some of the details of how to efficiently propagate records through their life cycle and moving database entries from write-optimized to read-optimized storage formats. In summary, the paper aims at illustrating how the SAP HANA database is able to efficiently work in analytical as well as transactional workload environments.

The usage of RFID and sensing technologies in supply chain management applications requires the automatic conversion of large amounts of raw data into manageable business process information. This has led to many performance and scalability issues in existing RFID infrastructures. We present an approach to alleviate these shortcomings based on a flexible system architecture that partially migrates business data processing towards the periphery.

In an emergency situation failure to respond in a timely manner poses a significant threat. Data needed for timely response comes from various sources and sensors. These individual data streams when viewed in isolation may appear irrelevant, however, when analyzed collectively may identify potential threats. An effective and timely response also requires collaboration and information sharing among various government agencies at all levels. This collaboration information sharing among agencies can be achieved using service-oriented architecture, where agencies provide access to their information resources and applications using Web services. Each of these agencies has its own rules/policies for providing their services. It is therefore, important to verify the correctness of the emergency response processes with respect to the rules/policies of the collaborating agencies involved in the execution of such processes. In this paper we present an approach which addresses the above challenges. Specifically, the proposed approach: a) employs multi stream data mining for identification of potential threats and disambiguation of alarms; b) provides a methodology for the discovery and selection of relevant Web services; c) employs a timed automata based verification methodology for determining the correctness of emergency response processes with respect to the rules of the collaborating agencies. We provide an overview of the initial implementation of the proposed approach.

Today's applications are increasingly composed out of services. Standardized protocols, such as SOAP, WSDL, and UDDI are used to discover and invoke remotely located services. Nowadays, improved resource capabilities of mobile devices, e.g. PDAs, smart phones, and sensor devices allow the execution of services even on these smaller computing devices. In comparison to traditional centralized process management, a decentralized, cooperative execution of services on embedded real-time systems leads to higher system scalability, better system response time and higher data accuracy. In this paper we describe an efficient way to deploy services onto highly distributed, mobile, and unreliable devices. To achieve an efficient resource tracking we utilize different group-based data retrieval strategies. Furthermore, we present a prototype system that implements our distributed service deployment algorithm and that evaluates our approach in terms of scalability for different network topologies.

Semantic Web technology is a promising first step for automated Web service discovery. Most current approaches for Web service discovery cater to semantic Web services, i.e., Web services that have associated semantic descriptions. It is unrealistic, however, to expect all new services to have associated semantic descriptions. Furthermore, the descriptions of the vast majority of already existing services do not have explicitly associated semantics. In this paper we present a novel approach for Web service discovery that combines semantic and statistical association metrics. Semantic metrics are based on the semantic aspects of relevant ontology. Statistical association metrics are based on the association aspects of Web services instances (their inputs and outputs). Specifically, our approach exploits semantic relationship ranking for establishing semantic relevance, and a hyperclique pattern discovery method for grouping Web service parameters into meaningful associations. These associations combined by the semantic relevance are then leveraged to discover and rank Web services

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