Genetic based clustering algorithms and applications.

by Lee Wing Kin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 81-90).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.iiiList of Figures --- p.viiList of Tables --- p.viiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Clustering --- p.1Chapter 1.1.1 --- Hierarchical Classification --- p.2Chapter 1.1.2 --- Partitional Classification --- p.3Chapter 1.1.3 --- Comparative Analysis --- p.4Chapter 1.2 --- Cluster Analysis and Traveling Salesman Problem --- p.5Chapter 1.3 --- Solving Clustering Problem --- p.7Chapter 1.4 --- Genetic Algorithms --- p.9Chapter 1.5 --- Outline of Work --- p.11Chapter 2 --- The Clustering Algorithms and Applications --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Traveling Salesman Problem --- p.14Chapter 2.2.1 --- Related Work on TSP --- p.14Chapter 2.2.2 --- Solving TSP using Genetic Algorithm --- p.15Chapter 2.3 --- Applications --- p.22Chapter 2.3.1 --- Clustering for Vertical Partitioning Design --- p.22Chapter 2.3.2 --- Horizontal Partitioning a Relational Database --- p.36Chapter 2.3.3 --- Object-Oriented Database Design --- p.42Chapter 2.3.4 --- Document Database Design --- p.49Chapter 2.4 --- Conclusions --- p.53Chapter 3 --- The Experiments for Vertical Partitioning Problem --- p.55Chapter 3.1 --- Introduction --- p.55Chapter 3.2 --- Comparative Study --- p.56Chapter 3.3 --- Experimental Results --- p.59Chapter 3.4 --- Conclusions --- p.61Chapter 4 --- Three New Operators for TSP --- p.62Chapter 4.1 --- Introduction --- p.62Chapter 4.2 --- Enhanced Cost Edge Recombination Operator --- p.63Chapter 4.3 --- Shortest Path Operator --- p.66Chapter 4.4 --- Shortest Edge Operator --- p.69Chapter 4.5 --- The Experiments --- p.71Chapter 4.5.1 --- Experimental Results for a 48-city TSP --- p.71Chapter 4.5.2 --- Experimental Results for Problems in TSPLIB --- p.73Chapter 4.6 --- Conclusions --- p.77Chapter 5 --- Conclusions --- p.78Chapter 5.1 --- Summary of Achievements --- p.78Chapter 5.2 --- Future Development --- p.80Bibliography --- p.8

Dynamic deployment of context-aware access control policies for constrained security devices

Securing the access to a server, guaranteeing a certain level of protection over an encrypted communication channel, executing particular counter measures when attacks are detected are examples of security requirements. Such requirements are identi ed based on organizational purposes and expectations in terms of resource access and availability and also on system vulnerabilities and threats. All these requirements belong to the so-called security policy. Deploying the policy means enforcing, i.e., con guring, those security components and mechanisms so that the system behavior be nally the one speci ed by the policy. The deployment issue becomes more di cult as the growing organizational requirements and expectations generally leave behind the integration of new security functionalities in the information system: the information system will not always embed the necessary security functionalities for the proper deployment of contextual security requirements. To overcome this issue, our solution is based on a central entity approach which takes in charge unmanaged contextual requirements and dynamically redeploys the policy when context changes are detected by this central entity. We also present an improvement over the OrBAC (Organization-Based Access Control) model. Up to now, a controller based on a contextual OrBAC policy is passive, in the sense that it assumes policy evaluation triggered by access requests. Therefore, it does not allow reasoning about policy state evolution when actions occur. The modi cations introduced by our work overcome this limitation and provide a proactive version of the model by integrating concepts from action speci cation languages

Algebraic Query Optimization in Database Systems (Algebraische Anfrageoptimierung in Datenbanksystemen)

The thesis investigates different problem classes in algebraic query optimization. For the problem of computing optimal left-deep processing trees with cross products for chain queries and ASI cost functions we present two efficient algorithms. Although, in practice both algorithms yield identical results we have not been able to prove this. For the case of acyclic query graphs, left-deep processing trees, expensive selection and join predicates and ASI cost functions we describe a polynomial time algorithm which is based on a job sequencing algorithm. The algorithm assumes that the set of expensive selections that can be applied directly to the base relations can be guessed. The cheapest plans can be found within the search space of bushy processing trees with cross products. We prove that the problem is NP-hard in this case. The rest of the thesis deals with the general problem of computing optimal bushy processing trees for arbitrary query graphs and expensive selection and join predicates. For this problem we present three efficient dynamic programming algorithms. Our algorithms can handle different join algorithms, split conjunctive predicates, and exploit structural information from the join graph to speed up computation. The time and space complexities of the algorithms are analyzed carefully and efficient implementations based on bitvector arithmetic are presented

Implicit Incremental Model Analyses and Transformations

When models of a system change, analyses based on them have to be reevaluated in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. This thesis proposes multiple, combinable approaches and a new formalism based on category theory for implicitly incremental model analyses and transformations. The advantages of the implementation are validated using seven case studies, partially drawn from the Transformation Tool Contest (TTC)

Development of new data partitioning and allocation algorithms for query optimization of distributed data warehouse systems

Distributed databases and in particular distributed data warehousing are becoming an increasingly important technology for information integration and data analysis. Data Warehouse (DW) systems are used by decision makers for performance measurement and decision support. However, although data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, the OLAP query response time is strongly affected by the volume of data need to be accessed from storage disks. Data partitioning is one of the physical design techniques that may be used to optimize query processing cost in DWs. It is a non redundant optimization technique because it does not replicate data, contrary to redundant techniques like materialized views and indexes. The warehouse partitioning problem is concerned with determining the set of dimension tables to be partitioned and using them to generate the fact table fragments. In this work an enhanced grouping algorithm that avoids the limitations of some existing vertical partitioning algorithms is proposed. Furthermore, a static partitioning algorithm that allows fragmentation at early stages of schema design is presented. The thesis also, investigates the performance of the data warehouse after implementing a combination of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques to horizontally partition the data warehouse star schema. It, then presents the experimentation and implementation results of the proposed algorithm. This research presented different approaches to optimize data fragments allocation cost using a greedy mathematical model and a combination of simulated annealing and genetic algorithm to determine the site by site allocation leading to optimal solutions for fragments distribution. Throughout this thesis, the term fragmentation and partitioning will be used interchangeably

Intelligent Business Process Optimization for the Service Industry

The company\u27s sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization

Intelligent Business Process Optimization for the Service Industry

The company's sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization

Test generation for high coverage with abstraction refinement and coarsening (ARC)

Testing is the main approach used in the software industry to expose failures. Producing thorough test suites is an expensive and error prone task that can greatly benefit from automation. Two challenging problems in test automation are generating test input and evaluating the adequacy of test suites: the first amounts to producing a set of test cases that accurately represent the software behavior, the second requires defining appropriate metrics to evaluate the thoroughness of the testing activities. Structural testing addresses these problems by measuring the amount of code elements that are executed by a test suite. The code elements that are not covered by any execution are natural candidates for generating further test cases, and the measured coverage rate can be used to estimate the thoroughness of the test suite. Several empirical studies show that test suites achieving high coverage rates exhibit a high failure detection ability. However, producing highly covering test suites automatically is hard as certain code elements are executed only under complex conditions while other might be not reachable at all. In this thesis we propose Abstraction Refinement and Coarsening (ARC), a goal oriented technique that combines static and dynamic software analysis to automatically generate test suites with high code coverage. At the core of our approach there is an abstract program model that enables the synergistic application of the different analysis components. In ARC we integrate Dynamic Symbolic Execution (DSE) and abstraction refinement to precisely direct test generation towards the coverage goals and detect infeasible elements. ARC includes a novel coarsening algorithm for improved scalability. We implemented ARC-B, a prototype tool that analyses C programs and produces test suites that achieve high branch coverage. Our experiments show that the approach effectively exploits the synergy between symbolic testing and reachability analysis outperforming state of the art test generation approaches. We evaluated ARC-B on industry relevant software, and exposed previously unknown failures in a safety-critical software component

Enhancing Search-Based Techniques with Information Control Dependencies

Software testing is a very important task during software development and it can be used to improve the quality and reliability of the software system. One potential way to reduce the cost and increase the efficiency of software testing is to generate test data automatically. Search-based approaches successfully generate unit tests for object-oriented programs, like Java. However, challenges, such as the large size of the search space, and the presence of complex predicates’ target branches, negatively affect the approaches, and, thus, cannot achieve high structural coverage for certain programs. The aim of this thesis is to propose enhancement techniques to improve the effectiveness of search based testing approaches and address the challenges posed by the object-oriented programs. Rather than randomly generating a sequence of method calls, our ongoing work is to focus on using static analysis to define the hidden data dependencies on predicates’ target branches (i.e., uncovered branches) and exploit method dependence relations (MDR) approach to precisely identify the method/constructors and its parameters. This method dependence information is employed to reduce the search space and used to guide the search toward regions that lead to full (or at least high) structural coverage

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible