Holger Schwarz

Improving the processing of decision support queries: the case for a DSS optimizer

Many decision support applications are built upon data mining and OLAP tools and allow users to answer information requests based on a data warehouse that is managed by a powerful DBMS. We focus on tools that generate sequences of SQL statements in order to produce the requested information. Our thorough analysis revealed that many sequences of queries that are generated by commercial tools are not very efficient. An optimized system architecture is suggested for these applications. The main component is a DSS optimizer that accepts previously generated sequences of queries and remodels them according to a set of optimization strategies, before they are executed by the underlying database system. The advantages of this extended architecture are discussed and a couple of appropriate optimization strategies are identified. Experimental results are given, showing that these strategies are appropriate to optimize query sequences of OLAP applications.

An Overview of SQL Support in Workflow Products

Over the last years, data management products as well as workflow products have established themselves as indispensable building blocks for advanced IT systems in almost all application areas. Recently, many vendors have created innovative product extensions that combine service-oriented frameworks with powerful workflow and data management capabilities. In this paper, we discuss several workflow products from different vendors with a specific focus on their SQL support. We provide a comparison based on a set of important data management patterns and illustrate the characteristics of various approaches by means of a running example.

Modeling and generating application logic for data-intensive web applications

This paper presents a new approach for the development of data-intensive web applications that depend on sophisticated application logic. E-Commerce web sites, on-line auction systems and large enterprise web portals fall into this category as they require comprehensive data access, data processing and data manipulation capabilities. However, existing methodologies mainly concentrate on modeling content, navigation and presentation aspects of read-only web sites. In our opinion these models are not sufficient to express complex operations that access or modify web application content. Therefore, we propose an additional Operation Model defining the operation logic of a web application. We show that based on this model a significant part of a web applications Operation Layer can be generated, still allowing the manual implementation of arbitrary additional functionality. We evaluate our approach and present experimental results based on a large example application for the area of innovation management.

Supporting manufacturing design by analytics, continuous collaborative process improvement enabled by the advanced manufacturing analytics platform

The manufacturing industry is faced with global competition making efficient, effective and continuously improved manufacturing processes a critical success factor. Yet, media discontinuities, the use of isolated analysis methods on local data sets as well as missing means for sharing analysis results cause a collaborative gap in Manufacturing Process Management that prohibits continuous process improvement. To address this challenge, this paper proposes the Advanced Manufacturing Analytics (AdMA) Platform that bridges the gap by integrating operational and process manufacturing data, defining a repository for analysis results and providing indication-based and pattern-based optimization techniques. Both the conceptual architecture underlying the platform as well as its current implementation are presented in this paper.

Deep Business Optimization: Making Business Process Optimization Theory Work in Practice

The success of most of today’s businesses is tied to the efficiency and effectiveness of their core processes. This importance has been recognized in research, leading to a wealth of sophisticated process optimization and analysis techniques. Their use in practice is, however, often limited as both the selection and the application of the appropriate techniques are challenging tasks. Hence, many techniques are not considered causing potentially significant opportunities of improvement not to be implemented. This paper proposes an approach to addressing this challenge using our deep Business Optimization Platform. By integrating a catalogue of formalized optimization techniques with data analysis and integration capabilities, it assists analysts both with the selection and the application of the most fitting optimization techniques for their specific situation. The paper presents both the concepts underlying this platform as well as its prototypical implementation.

Prescriptive Analytics for Recommendation-Based Business Process Optimization

Continuously improved business processes are a central success factor for companies. Yet, existing data analytics do not fully exploit the data generated during process execution. Particularly, they miss prescriptive techniques to transform analysis results into improvement actions. In this paper, we present the data-mining-driven concept of recommendation-based business process optimization on top of a holistic process warehouse. It prescriptively generates action recommendations during process execution to avoid a predicted metric deviation. We discuss data mining techniques and data structures for real-time prediction and recommendation generation and present a proof of concept based on a prototypical implementation in manufacturing.

Automated Process Decision Making Based on Integrated Source Data

Decision activities are frequently responsible for a major part of a process’s duration and resource consumption. The automation of these activities hence holds the promise of significant cost and time savings, however, only if the decision quality does not suffer. To achieve this, it is required to consider data from diverse sources that go beyond the process audit log, which is why approaches relying solely on it are likely to yield sub-optimal results. We therefore present in this paper an approach to process decision automation that incorporates data integration techniques, enabling significant improvements in decision quality.

Building the data warehouse of frequent itemsets in the DWFIST approach

Some data mining tasks can produce such great amounts of data that we have to cope with a new knowledge management problem. Frequent itemset mining fits in this category. Different approaches were proposed to handle or avoid somehow this problem. All of them have problems and limitations. In particular, most of them need the original data during the analysis phase, which is not feasible for data streams. The DWFIST (Data Warehouse of Frequent ItemSets Tactics) approach aims at providing a powerful environment for the analysis of itemsets and derived patterns, such as association rules, without accessing the original data during the analysis phase. This approach is based on a Data Warehouse of Frequent Itemsets. It provides frequent itemsets in a flexible and efficient way as well as a standardized logical view upon which analytical tools can be developed. This paper presents how such a data warehouse can be built.

A Multi-Tier Architecture for High-Performance Data Mining

Data mining has been recognised as an essential element of decision support, which has increasingly become a focus of the database industry. Like all computationally expensive data analysis applications, for example Online Analytical Processing (OLAP), performance is a key factor for usefulness and acceptance in business. In the course of the CRITIKAL1 project (Client-Server Rule Induction Technology for Industrial Knowledge Acquisition from Large Databases), which is funded by the European Commission, several kinds of architectures for data mining were evaluated with a strong focus on high performance. Specifically, the data mining techniques association rule discovery and decision tree induction were implemented into a prototype. We present the architecture developed by the CRITIKAL consortium and compare it to alternative architectures.

Towards situation-aware adaptive workflows: SitOPT — A general purpose situation-aware workflow management system

Workflows are an established IT concept to achieve business goals in a reliable and robust manner. However, the dynamic nature of modern information systems, the upcoming Industry 4.0, and the Internet of Things increase the complexity of modeling robust workflows significantly as various kinds of situations, such as the failure of a production system, have to be considered explicitly. Consequently, modeling workflows in a situation-aware manner is a complex challenge that quickly results in big unmanageable workflow models. To overcome these issues, we present an approach that allows workflows to become situation-aware to automatically adapt their behavior according to the situation they are in. The approach is based on aggregated context information, which has been an important research topic in the last decade to capture information about an environment. We introduce a system that derives high-level situations from lower-level context and sensor information. A situation can be used by different situation-aware workflows to adapt to the current situation in their execution environment. SitOPT enables the detection of situations using different situation-recognition systems, exchange of information about detected situations, optimization of the situation-recognition, and runtime adaption and optimization of situation-aware workflows based on the recognized situations.