Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Comprehensive security in data processing systems : the state of the art /

"UIUCDCS-R-75-725"

A programming language for the parallel processing of pictures /

Thesis (M.S.)--University of Illinois at Urbana-Champaign, 1966.

Parallel processing of ordinary programs /

Bibliography: p. 89-96.

Tree height reduction for parallel processing of blocks of Fortran assignment statements.

Thesis (M.S.)--University of Illinois.

Parallel processing research in the former Soviet Union /

Mode of access: Internet.

Management influence on the design of data processing systems, a case study.

Mode of access: Internet.

Automatic data-processing systems;

Mode of access: Internet.

Considerations in the selection of security measures for automatic data processing systems /

"Contributed to the Federal Information Processing Standards Task Group 15 - Computer Systems Security" -t.p.

The application of parallel processing methods to vibrational analysis

The trend in modal extraction algorithms is to use all the available frequency response functions data to obtain a global estimate of the natural frequencies, damping ratio and mode shapes. Improvements in transducer and signal processing technology allow the simultaneous measurement of many hundreds of channels of response data. The quantity of data available and the complexity of the extraction algorithms make considerable demands on the available computer power and require a powerful computer or dedicated workstation to perform satisfactorily. An alternative to waiting for faster sequential processors is to implement the algorithm in parallel, for example on a network of Transputers. Parallel architectures are a cost effective means of increasing computational power, and a larger number of response channels would simply require more processors. This thesis considers how two typical modal extraction algorithms, the Rational Fraction Polynomial method and the Ibrahim Time Domain method, may be implemented on a network of transputers. The Rational Fraction Polynomial Method is a well known and robust frequency domain 'curve fitting' algorithm. The Ibrahim Time Domain method is an efficient algorithm that 'curve fits' in the time domain. This thesis reviews the algorithms, considers the problems involved in a parallel implementation, and shows how they were implemented on a real Transputer network.

Performance management of event processing systems

This thesis is a study of performance management of Complex Event Processing (CEP) systems. Since CEP systems have distinct characteristics from other well-studied computer systems such as batch and online transaction processing systems and database-centric applications, these characteristics introduce new challenges and opportunities to the performance management for CEP systems. Methodologies used in benchmarking CEP systems in many performance studies focus on scaling the load injection, but not considering the impact of the functional capabilities of CEP systems. This thesis proposes the approach of evaluating the performance of CEP engines’ functional behaviours on events and develops a benchmark platform for CEP systems: CEPBen. The CEPBen benchmark platform is developed to explore the fundamental functional performance of event processing systems: filtering, transformation and event pattern detection. It is also designed to provide a flexible environment for exploring new metrics and influential factors for CEP systems and evaluating the performance of CEP systems. Studies on factors and new metrics are carried out using the CEPBen benchmark platform on Esper. Different measurement points of response time in performance management of CEP systems are discussed and response time of targeted event is proposed to be used as a metric for quality of service evaluation combining with the traditional response time in CEP systems. Maximum query load as a capacity indicator regarding to the complexity of queries and number of live objects in memory as a performance indicator regarding to the memory management are proposed in performance management of CEP systems. Query depth is studied as a performance factor that influences CEP system performance.