Analytical Processing Systems Research Articles

Implementation of a Mesoscopic Mechanical Model for the Shear Fracture Process Analysis of Masonry

This short paper will present a two-dimensional (2D) model of masonry material. This mesoscopic mechanical model is suitable to simulate the behavior of masonry. Considering the heterogeneity of masonry material, based on the damage mechanics and elastic-brittle theory, the new developed Material Failure Process Analysis (MFPA2D) system was brought out to simulate the cracking process of masonry, which was considered as a three-phase composite of the block phase, the mortar phase and the block-mortar interfaces. The crack propagation processes simulated with this model shows good agreement with those of experimental observations. It has been found that the shear fracture of masonry observed at the macroscopic level is predominantly caused by tensile damage at the mesoscopic level. Some brittle materials are so weak in tension relative to shear that tensile rather than shear fractures are generated in pure shear loading.

Hierarchies in a multidimensional model: From conceptual modeling to logical representation

Hierarchies are used in data warehouses (DWs) and on-line analytical processing (OLAP) systems to see data at different levels of detail. However, many kinds of hierarchies arising in real-world situations are not addressed by current OLAP systems. Further, there is still no agreement on a conceptual model for DW and OLAP design that offers both a graphical representation and a formal definition. In this paper, we formally define the MultiDimER model, a conceptual multidimensional model that allows to represent facts with measures as well as the different kinds of hierarchies already classified in our previous work [E. Malinowski, E. Zimányi, OLAP hierarchies: a conceptual perspective, in: Proceedings of the 16th International Conference on Advanced Information Systems Engineering, 2004, pp. 477–491]. We also present the mapping of such hierarchies to the relational model, as well as their implementation in commercial DW products.

Numerical Study on Cracking Process of Masonry Structure

Masonry structure is heterogeneous and has been widely used in building and construction engineering. The study on cracking pattern of masonry structure is significant to engineering design. Many previous investigations on the failure process of masonry structure are usually based on the homogenization technique by selecting a typical unit of masonry to serve as a representative volume. This kind of numerical analysis neglects the mesoscopic heterogeneous structure, which cannot capture the full cracking process of masonry structures. The cracking process of masonry structure is dominantly affected by its heterogeneous internal structures. In this paper, a mesoscopic mechanical model of masonry material is developed to simulate the behavior of masonry structure. Considering the heterogeneity of masonry material, based on the damage mechanics and elastic-brittle theory, the new developed Material Failure Process Analysis (MFPA2D) system was put forward to simulate the cracking process of masonry structure, which was considered as a two-phase composite of block and mortar phases. The crack propagation processes simulated with this model shows good agreement with those of experimental observations. The numerical results show that numerical analysis clearly reflect the modification, transference and their interaction of the stress field and damage evolution process which are difficult to achieve by physical experiments. It provides a new method to research the forecast theory of failure and seismicity of masonry. It has been found that the fracture of masonry observed at the macroscopic level is predominantly caused by tensile damage at the mesoscopic level.

ASM-based design of data warehouses and on-line analytical processing systems

On-line analytical processing (OLAP) systems deal with analytical tasks in businesses. As these tasks do not depend on the latest updates by transactions, it is assumed that the data used in OLAP systems are kept in a data warehouse, which separates the input from operational databases from the outputs to dialogue interfaces for OLAP. Data Warehouses and OLAP systems are a promising area for the application of Abstract State Machines (ASMs). In this article a layered ground model specification for data warehouses and OLAP system is presented that is based explicitly on the fundamental idea of separating input from operational databases from output to OLAP systems. On this basis we start defining formal refinement rules for such systems. As these refinement rules are formally correct they enable a formal method for the high-quality design of data warehouses and OLAP systems that can be applied without knowing mathematical details of the ASM formalism. Furthermore, we discuss pragmatic guidelines for the application of such rules.

A comprehensive approach for evaluating telemedicine-delivered multidisciplinary breast cancer meetings in southern Scotland

Multidisciplinary team (MDT) meetings for decisions on cancer management are a cornerstone of UK cancer policy. We have proposed a comprehensive methodology to assess the clinical and economic effectiveness of telemedicine in this setting, which is being tested in a randomized breast cancer trial. Pre- and post-telemedicine assessment includes attitudes to and expectations of telemedicine, based on semistructured interviews. The communication content of videotapes of the MDT meeting is being scored using Borgatta's revised Interaction Process Analysis System. The technical performance of the telemedicine equipment is reported on a standardized pro forma. A short questionnaire captures key elements of professional satisfaction for each patient discussion (consensus on future management, confidence in and sharing of decision), added value of linkage, group atmosphere, overall conduct of the meeting and compliance with SIGN guidelines. A cost-minimization analysis will be used for economic assessment.

Supporting mobile decision making with association rules and multi-layered caching

We describe a methodology and a prototype implementation of an online analytical processing system for mobile devices. The system guides the user to narrow down the search space using association rules. We also describe multi-layered caching techniques to improve performance and increase system utilization even in the presence of disconnections. The system is built using a three-tier architecture comprising of a data warehouse, a middle-tier server, and client mobile devices. Finally we conducted a series of simulation experiments to evaluate the performance of our association rule-based system and the multi-layered caching.

Integrated Electroplating System Modeling and Simulation for Near Zero Discharge of Chemicals and Metals

The optimality of design and operation of an electroplating system determines largely coating quality, productivity, and waste reduction efficiency. Industrial practice shows that, in a usual operation, the solution loss from an electroplating unit through drag-out can be as high as 30% of overall consumption. This has dramatically increased operating cost as well as waste treatment cost. On the other hand, plating quality in terms of coating thickness on workpieces is always a concern in plants. To improve the economic and environmental performance, a key step is to have a deep understanding of the system. Model-based simulation has proven to be a cost-effective approach along this venue. This paper introduces a fundamental-based general modeling methodology for characterizing an integrated electroplating system that consists of a plating unit and a solution recovery subsystem. The methodology allows detailed system analysis and complete process information integration, which will be crucial for optimal design and operation of a closed-loop electroplating for prevention of plating solution loss and assurance of coating thickness on workpieces. A case study on an alkali zinc electroplating system will demonstrate the efficacy of the model-based design and operation approach.

The Potential of Group Support Systems (Gss) to Enhance Systems Analysis and Design Processes and Outcomes

This paper focuses on how Group Support System (GSS) can be combined to effectively improve the Systems Analysis and Design (SA&D) process. The objective is to demonstrate the potential benefits of using GSS with SA&D, especially when used with JAD, CASE, data modeling, process modeling, and BPR. Bringing off the benefits of GSS used with SA&D, several limitations and issues are also explored. Finally, future research areas of using GSS with SA&D are explored in relation to culture, distributed work teams, and artificial intelligence.

Strategies for implementing decision technology systems successfully: a field study in army housing management

A decision technology system (DTS) has been proposed to provide direct support for the entire decision-making process. In the 1990s, the concept was implemented in stages to facilitate US Army housing management. This implementation has been the first successful field application of the DTS theory. Recent enhancements confirmed earlier insights and offered important new lessons in developing, implementing, and benefiting from DTSs. This paper discusses the original insights and fresh lessons. To provide a context, the article first briefly describes the enhancements and presents a typical, but streamlined, system session. The paper then summarizes the documented gains from implementing the DTS theory, reports the utilized strategies, and infers the implications for the systems analysis and design process.

Demand-driven Evolution of IT Systems in Healthcare

To analyze and to optimize interdisciplinary clinical processes, to introduce an IT-supported model for demand-driven system evolution in healthcare, and to demonstrate the feasibility of the approach for a clinical example and to present an evaluation. System evolution and change management are viewed as two sides of the same coin, thus formal methods for process analysis and IT system evolution were embedded into a goal-oriented change management model. Based on a process model, a Failure Mode and Effects Analysis (FMEA) and a computer simulation were performed. A tool for rapid application development (RAD) was used to incrementally improve the healthcare information system according to newly arising needs. Each of the formal methods used contributed to the successful reorganization of the interdisciplinary clinical process. An evaluation demonstrated significant improvements. An integrated IT application was implemented to support the optimized process. Process improvement is feasible and effective when formal methods for process analysis and requirements specification are used in a reasonable and goal-oriented way. It might be necessary to trade off costs and benefits or simplify a given method in the context of a particular project. As the same information is utilized in different tools, it is supposed that the efforts for process analysis, documentation and implementation of adapted applications could be reduced if different tools were integrated and based on a single coherent reference model for description of clinical processes.

Digital content management: the search for a content management system

Digital content management system is a software system that provides preservation, organization and dissemination services for digital collections. By adapting the systems analysis process, the University of Arizona Library analyzed its needs and developed content management system requirements for finding a suitable information system that addresses the increasing needs of digital content management. Dozens of commercial and open source candidates were examined to match against the requirements. This article provides detailed analysis of three major players (Greenstone, Fedora, and DSpace) in key areas of digital content management: preservation, metadata, access, and system features based on the needs of the University of Arizona Library. This paper describes the process used to analyze and evaluate potential candidates and includes results of analysis to illuminate the process.

Cardinality-based inference control in data cubes*

This paper addresses the inference problem in on-line analytical processing (OLAP) systems. The inference problem occurs when the exact values of sensitive attributes can be determined through answers to OLAP queries. Most existing inference control methods are computationally expensive for OLAP systems, because they ignore the special structures of OLAP queries. By exploiting such structures, we derive cardinality-based sufficient conditions for safe OLAP data cubes, Specifically, data cubes are safe, from inferences if their core cuboids are dense enough, in the sense that the number of known values is under a tight bound. We then apply the sufficient conditions on the basis of a three-tier inference control model. The model introduces an aggregation tier between data and queries. The aggregation tier represents a collection of safe data cubes that are pre-computed over a partition of the data using the proposed sufficient conditions. The aggregation tier is then used to provide users with inference-free queries. Our approach mitigates the performance penalty of inference control, because partitioning the data yields smaller input to inference control algorithms, pre-computing the aggregation tier reduces on-line delay, and using cardinality-based conditions guarantees linear-time complexity.

Supporting systems analysis and design through fisheye views

Demonstrating the usefulness of integrating context-based views into the systems analysis and design process.

Parallel ROLAP Data Cube Construction on Shared-Nothing Multiprocessors

The pre-computation of data cubes is critical to improving the response time of on-line analytical processing (OLAP) systems and can be instrumental in accelerating data mining tasks in large data warehouses. In order to meet the need for improved performance created by growing data sizes, parallel solutions for generating the data cube are becoming increasingly important. The paper presents a parallel method for generating data cubes on a shared-nothing multiprocessor. Since no (expensive) shared disk is required, our method can be used on low cost Beowulf style clusters consisting of standard PCs with local disks connected via a data switch. Our approach uses a ROLAP representation of the data cube where views are stored as relational tables. This allows for tight integration with current relational database technology. We have implemented our parallel shared-nothing data cube generation method and evaluated it on a PC cluster, exploring relative speedup, local vs. global schedule trees, data skew, cardinality of dimensions, data dimensionality, and balance tradeoffs. For an input data set of 2000000 rows (72 Megabytes), our parallel data cube generation method achieves close to optimal speedup; generating a full data cube of /spl ap/227 million rows (5.6 Gigabytes) on a 16 processors cluster in under 6 minutes. For an input data set of 10,000,000 rows (360 Megabytes), our parallel method, running on a 16 processor PC cluster, created a data cube consisting of /spl ap/846 million rows (21.7 Gigabytes) in under 47 minutes.

Supporting dimension updates in an OLAP server

Commercial on line analytical processing (OLAP) systems usually treat OLAP dimensions as static entities. In practice, dimension updates are often needed to adapt the warehouse to changing requirements. In earlier work, we defined a taxonomy for these dimension updates and a minimal set of operators to perform them. In this paper we present TSOLAP, an OLAP server supporting fully dynamic dimensions. TSOLAP conforms to the OLE DB for OLAP norm, so it can be used by any client application based on this norm, and can use as back-end any conformant relational server. We incorporate dimension update support to MDX, Microsoft's language for OLAP, and introduce TSShow, a visualization tool for dimensions and data cubes. Finally, we present the results of a real-life case study in the application of TSOLAP to a medium-sized medical center.

Usability-based caching of query results in OLAP systems

In this paper we propose a new cache management scheme for online analytical processing (OLAP) systems based on the usability of query results in rewriting and processing other queries. For effective admission and replacement of OLAP query results, we consider the benefit of query results not only for recently issued queries but for the expected future queries of a current query. We exploit semantic relationships between successive queries in an OLAP session, which are derived from the interactive and navigational nature of OLAP query workloads, in order to classify and predict subsequent future queries. We present a method for estimating the usability of query results for the representative future queries using a probability model for them. Experimental evaluation shows that our caching scheme using the past and future usability of query results can reduce the cost of processing OLAP query workloads effectively only with a small cache size and outperforms the previous caching strategies for OLAP systems.

Decision support in the absence of necessary information and models by means of OLAP-systems: organisational and managerial perspectives

Formal methodologies of decision support and formal presentation of decision making processes commonly imply development (or selection) of appropriate algorithms and models (preferably mathematical) and their subsequent practical usage. But rapid changes of economical, market and technological conditions often make it impossible to develop comprehensive decision making models and corresponding procedures thereby enforcing decision making under uncertainty. As a rule, in such situations managers and analysts may rely mostly on information about facts that took place in the past. That is why flexible access to such data is crucial for practical management. Our approach sets up a paradigm for the integration of On-Line Analytical Processing systems into managerial processes with respect to support urgent decision making in the situation when development of comprehensive models is impossible. We also discuss corresponding organisational framework aimed to ensure practical realisation of this paradigm.

웹 기반의 OLAP 메타데이터 교환 시스템의 설계 및 구현

As the importance of knowledge management is being recognized, there is a significant amount of increase for interest in data warehousing. On-Line Analytical Processing (OLAP) systems can effectively make use of data warehouses. Although there are many commercial OLAP products, they have been developed without any kind of standard resulting in poor data exchange and difficulty in interfacing among the OLAP products. In this paper we propose an OLAP metadata interchange model that can be used among different OLAP products and have implemented an OLAP metadata interchange system that can interchange the cubes created from the metadata. XML is used for the OLAP metadata model and the user interface is Web-based, which makes it easier to interchange metadata among different OLAP products. Users can experience the different analysis environments of different products without the need to learn the complex cube creation process for each product. By extending this research to design a common query language that can be used among OLAP products, OLAP products should be able to more easily talk to one another.

Information System Analysis for a Large Construction Firm: A Case Study in Turkey

The subject of this paper is an information system (IS) analysis study for a Turkish contracting firm realizing large scale projects both in Turkey and abroad. The first purpose of this paper is to expose the conceptual framework of the IS analysis approach stated above and method in detail, and the steps and the tools used to do an IS analysis in a contracting firm. The relevant literature tells about numerous strategies and methods directed to IS analysis and development. However, most of these methods are not handled from the operational aspect. Therefore, the authors hope to make a contribution to this area with this paper that explains the system analysis process from the operational point of view. The second purpose is to explain the problems encountered by the authors in the implementation of the method and share the experience gained by the study and thereby contribute to debates concerning methodology related to IS analysis in construction firms. Finally, the third purpose is to expound the problems of the existing IS used by the contracting firm and thus establish the main points that should be kept in mind during IS analysis and development in contracting firms.

A Dynamic Parallel Data-Computing Environment for Cross-Sensor Satellite Data Merger and Scientific Analysis

Abstract The Data Processing and Error Analysis System (DPEAS) is a dynamic parallel data processing system for cross-sensor satellite data merger and analysis. Using a peer-to-peer methodology, DPEAS is able to distribute and simplify the near-real-time merger of multiple datasets from several sensors and satellite platforms. The organization of the system and its practical benefits for research and operational use are discussed. Using several simplifying constructs, the technical requirements placed upon the end user are minimized, while maintaining high scalability and reliability of the system. Thus, the system encourages the flow of research into operations by making the system environment mobile, rather than placing the transferability burden on the research development effort. Several reliability enhancements, such as cluster failover and automatic file replication services, are incorporated into the system. The technical aspects of the various feature implementations are discussed in detail. Futur...