Veit Köppen

Combining schema and level-based matching for web service discovery

Due to the availability of huge number ofWeb services (WSs), finding an appropriate WS according to the requirement of a service consumer is still a challenge. In this paper, we present a new and flexible approach, called SeqDisc, that assesses the similarity between WSs. In particular, the approach exploits the Prufer encoding method to represent WSs as sequences capturing both semantic and structure information of service descriptions. Based on the sequence representation, we develop an efficient sequence-based schema matching approach to measure the similarity between WSs. A set of experiments is conducted on real data sets, and the results confirm the performance of the proposed solution.

QuEval: beyond high-dimensional indexing à la carte

In the recent past, the amount of high-dimensional data, such as feature vectors extracted from multimedia data, increased dramatically. A large variety of indexes have been proposed to store and access such data efficiently. However, due to specific requirements of a certain use case, choosing an adequate index structure is a complex and time-consuming task. This may be due to engineering challenges or open research questions. To overcome this limitation, we present QuEval, an open-source framework that can be flexibly extended w.r.t. index structures, distance metrics, and data sets. QuEval provides a unified environment for a sound evaluation of different indexes, for instance, to support tuning of indexes. In an empirical evaluation, we show how to apply our framework, motivate benefits, and demonstrate analysis possibilities.

Business Process Improvement Framework and Representational Support

Business process management and improvement are vital for enterprises in competitive environments. Understanding of a process is a pre-requisite and important step for improvement. Interaction between humans, computers, and business objects provide excellent opportunities for knowledge extraction. However, the specification of a framework is required for business process improvement, which extends from data collection, analytical methods, storage, and representation of knowledge. The process models conceived for information system development are not sufficient for post execution analysis and improvement. In this paper, we specify such a framework briefly and focus on providing representational support for business process improvement. The main objective is to improve the overall improvement process by providing enriched graphical process models. Furthermore, we use a case study to explain the proposed usage and extensions of an existing modeling language for business process improvement.

Applying process mining in SOA environments

Process mining is an emerging analysis technique, which extracts process knowledge from data and provides various benefits to organizations. In Service Oriented Computing environment, different services collaborate with others to carry out the operations and therefore overall picture of operations and execution is not clear. Process mining extracts the information from log files of systems, as recorded during executions, and depicts the reality. In order to apply process mining, extraction of process trace data from log files is a pre-requisite step. A case study demonstrates the practical applicability of our proposed framework for extraction of the process trace data from application systems and integration portals.

Using software product lines for runtime interoperability

Today, often small, heterogeneous systems have to cooperate in order to fulfill a certain task. Interoperability between these systems is needed for their collaboration. However, achieving this interoperability raises several problems. For example, embedded systems might induce a higher probability for a system failure due to constrained power supply. Nevertheless, interoperability must be guaranteed even in scenarios where embedded systems are used. To overcome this problem, we use services to abstract the functionality from the system which realizes it. We outline how services can be generated using software product line techniques to bridge the heterogeneity of cooperating systems. Additionally, we address runtime changes of already deployed services to overcome system failures. In this paper, we show the runtime adaption process of these changes which includes the following two points. First, we outline why feature-oriented programming is appropriate in such scenarios. Second, we describe the runtime adaption process of services with feature-oriented programming.

An Architecture for Interoperability of Embedded Systems and Virtual Reality

Abstract Virtual Reality enhances the development process of complex and inter-operating products due to bringing existing systems together with virtual prototypes. The modeling of existing products within the virtual reality environment and furthermore the properties of products and product combination are important factors for success in a product life cycle. A reduction of effort for modeling of existing products and simulation of properties can be achieved, when systems and their properties are transported to the virtual reality environment. In this paper, we present a service-oriented architecture for embedded systems and virtual reality. The multiplicity of interfaces, protocols, and hardware and software aspects requires an architecture that overcomes the related difficulties to increase efficiency. Service-oriented architectures make different scenarios in the product life cycle possible, whereas the implementation effort for embedded systems is reduced due to software reuse.

Service variability patterns

Service-oriented computing (SOC) increases flexibility of IT systems and helps enterprises to meet their changing needs. Different methods address changing requirements in service-oriented environment. Many solutions exist to address variability, however, each solution is tailored to a specific problem, e.g. at one specific layer in SOC. We survey variability mechanisms from literature and summarize solutions, consequences, and possible combinations in a pattern catalogue. Based on the pattern catalogue, we compare different variability patterns and their combinations. Our catalogue helps to choose an appropriate technique for the variability problem at hand and illustrates its consequences in SOC.

A decision model to select the optimal storage architecture for relational databases

Requirements for database systems differ from small-scale database programs for embedded devices with minimal footprint to large-scale on-line analytical processing applications. For relational database management systems, two storage architectures have been introduced: a) row-oriented architecture and b) column-oriented architecture. In this paper, we present a query decomposition approach to evaluate database operations with respect to their performance according to the storage architecture. We map decomposed queries to workload patterns which contain aggregated database statistics. Further, we develop our complementary decision models which advise the selection of the optimal storage architecture for a given application domain. The first decision model improves the performance of running systems (on-line). The second and third decision model advise an efficient database design or decide which architecture is more suitable for a given application domain (off-line).

Toward variability management to tailor high dimensional index implementations

The increasing amount of complex data requires a solution to store and query these data efficiently. One possibility to speed-up various query types is the application of high dimensional index structures. In prior work, we introduced QuEval as platform to evaluate these indexes for user-defined use cases. Our design allows to easily extend QuEval with new index structure implementations. However, based on our experiences, we encountered severe challenges by tailoring index structure implementations to specific use cases. In particular, we face challenges to manage several similar implementation variants of the same index. In this paper, we consequently show benefits and drawbacks that emphasize the necessity to tailor index structure implementations with the help of a short evaluation study. Finally, we outline approaches for adequate variability management to address the aforementioned drawbacks.

A Layered Architecture for Enterprise Data Warehouse Systems

The architecture of Data Warehouse systems is described on basis of so-called reference architectures. Today’s requirements to Enterprise Data Warehouses are often too complex to be satisfactorily achieved by the rather rough descriptions of this reference architecture. We describe an architecture of dedicated layers to face those complex requirements, and point out additional expenses and resulting advantages of our approach compared to the traditional one.