6 research outputs found
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NON-VON's Performance on Certain Database Benchmarks
In a paper by Hawthorn and DeWitt, the projected performance of several proposed database machines was examined for three relational database queries. The present paper investigates the performance of a massively parallel machine called NON-VON for the same queries under comparable assumptions. In the case of simple queries, a NON-VON machine of comparable size to those considered by Hawthorn and DeWitt is found to be somewhat faster than the fastest machines examined in their study; for a more complex database operation, NON-VON is shown to be five to ten times faster than the fastest of these machines
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Image Understanding Algorithms on Fine-Grained Tree-Structured SIMD Machines
An Important goal for researchers In computer vision is the construction vision systems that Interpret Image data in real time. Such systems typically require a large amount of computation for processing raw Image data at the lowest level, and for sophisticated decision making at the highest level Recent advances In VLSI circuitry· have led to several proposals for parallel architectures for computer vision systems. In this theSIS. we demonstrate that fine-grained tree-structured SIMD machines, which have favorable characteristics for efficient VLSI Implementation, can be used for the rapid execution of a wide range of Image understanding tasks We also Identify the limitations of these architectures and propose methods to ameliorate these difficulties. The NON-VON supercomputer, currently being constructed at Columbia University, is an example of such an architecture. The major contribution of this thesis IS the development and analysis of several parallel Image understanding algorithms for the class of architectures under consideration The algorithms developed In this research have been selected to span different levels of computer vision tasks They Include Image correlation, hlstogrammlng, connected component labeling, the computation of geometric properties, set operations, the Hough transform
method for detecting object boundaries, and the correspondence problem In
moving light display applications. The algorithms Incorporate novel approaches to reduce the effects of communication bottleneck usually associated With tree architecture
Were the 1982 Merger Guidelines Old News?
This paper examines the impact of the 1982 Department of Justice Merger Guidelines on the stock market prices of publicly traded firms in the United States. We argue that those Guidelines were perceived by the market as a real change in enforcement policy that would result in substantial deregulation of mergers throughout the economy. We conduct an event study of S&P 500 firms to test this hypothesis and find evidence of a significant positive effect on the stock prices of firms in moderately concentrated industries subject to antitrust regulation, the firms for which the 1982 Guidelines articulate a substantially less intrusive enforcement policy. However, the announcement does not have any significant effect on firms in less concentrated industries or those that are highly concentrated. These results are robust to a number of different sensitivity analyses and thus we conclude that market actors believed the 1982 Guidelines contained new information
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Transaction behaviour in large database environments: A methodological approach
This thesis presents the CITY benchmark, a database benchmark that fairly represents On-Line Transaction Processing (OLTP) environments. It analyses the most widely used benchmarks in general putting more emphasis on the Wisconsin benchmark and the Transaction Processing Council (TPC) benchmarks (TPC-A, TPC- B and TPC-C) in particular. It also presents an empirical approach to examine the workload of those benchmarks and discovered several technical limitations in their scripts. The thesis also presents an investigation of on-line transactions in large database environments. The tested environments were three of the largest organisations in the UK, those organisations were different in objectives and activities. The investigation identified on-line transaction behaviour and defined the salient characteristics of databases in high-volume transaction environments. The findings from those studies established the basis of a transaction and set of tables that are representative of them. The CITY benchmark design is directly driven from the findings from the empirical studies. The benchmark design took into consideration all the critiques directed towards the TPC benchmarks A, B and C. It is the first benchmark that is designed as a result of studying the behaviour of on-line transactions and databases in large database environments. The CITY benchmark is mainly designed to test and compare database systems performance in high-volume transaction environments (OLTP).
The work revealed the salient characteristics of large database environments and identified a typical behaviour of on-line transaction in OLTP environments. This research has clearly shown that the TPC benchmarks are not representative to the domain of high-volume transactions environments (OLTP) and it explained why they could be misleading if used to test database management systems in this domain. Additionally, this thesis presents a database performance evaluation methodology that is based on in-depth studies in large database environments
A CONTENT-ADDRESSABLE-MEMORY ASSISTED INTRUSION PREVENTION EXPERT SYSTEM FOR GIGABIT NETWORKS
Cyber intrusions have become a serious problem with growing frequency and complexity. Current Intrusion Detection/Prevention Systems (IDS/IPS) are deficient in speed and/or accuracy. Expert systems are one functionally effective IDS/IPS method. However, they are in general computationally intensive and too slow for real time requirements. This poor performance prohibits expert system's applications in gigabit networks. This dissertation describes a novel intrusion prevention expert system architecture that utilizes the parallel search capability of Content Addressable Memory (CAM) to perform intrusion detection at gigabit/second wire speed. A CAM is a parallel search memory that compares all of its entries against input data in parallel. This parallel search is much faster than the serial search operation in Random Access Memory (RAM). The major contribution of this thesis is to accelerate the expert system's performance bottleneck "match" processes using the parallel search power of a CAM, thereby enabling the expert systems for wire speed network IDS/IPS applications. To map an expert system's Match process into a CAM, this research introduces a novel "Contextual Rule" (C-Rule) method that fundamentally changes expert systems' computational structures without changing its functionality for the IDS/IPS problem domain. This "Contextual Rule" method combines expert system rules and current network states into a new type of dynamic rule that exists only under specific network state conditions. This method converts the conventional two-database match process into a one-database search process. Therefore it enables the core functionality of the expert system to be mapped into a CAM and take advantage of its search parallelism.This thesis also introduces the CAM-Assisted Intrusion Prevention Expert System (CAIPES) architecture and shows how it can support the vast majority of the rules in the 1999 Lincoln Lab's DARPA Intrusion Detection Evaluation data set, and rules in the open source IDS "Snort". Supported rules are able to detect single-packet attacks, abusive traffic and packet flooding attacks, sequences of packets attacks, and flooding of sequences attacks. Prototyping and simulation have been performed to demonstrate the detection capability of these four types of attacks. Hardware simulation of an existing CAM shows that the CAIPES architecture enables gigabit/s IDS/IPS
Action planning and assessment in guidance contexts: how can I understand and support these processes while working with colleagues in Further Education colleges and career service provision in Avon?
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