962 research outputs found

    A Nine Month Progress Report on an Investigation into Mechanisms for Improving Triple Store Performance

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    This report considers the requirement for fast, efficient, and scalable triple stores as part of the effort to produce the Semantic Web. It summarises relevant information in the major background field of Database Management Systems (DBMS), and provides an overview of the techniques currently in use amongst the triple store community. The report concludes that for individuals and organisations to be willing to provide large amounts of information as openly-accessible nodes on the Semantic Web, storage and querying of the data must be cheaper and faster than it is currently. Experiences from the DBMS field can be used to maximise triple store performance, and suggestions are provided for lines of investigation in areas of storage, indexing, and query optimisation. Finally, work packages are provided describing expected timetables for further study of these topics

    A Comprehensive Survey on Database Management System Fuzzing: Techniques, Taxonomy and Experimental Comparison

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    Database Management System (DBMS) fuzzing is an automated testing technique aimed at detecting errors and vulnerabilities in DBMSs by generating, mutating, and executing test cases. It not only reduces the time and cost of manual testing but also enhances detection coverage, providing valuable assistance in developing commercial DBMSs. Existing fuzzing surveys mainly focus on general-purpose software. However, DBMSs are different from them in terms of internal structure, input/output, and test objectives, requiring specialized fuzzing strategies. Therefore, this paper focuses on DBMS fuzzing and provides a comprehensive review and comparison of the methods in this field. We first introduce the fundamental concepts. Then, we systematically define a general fuzzing procedure and decompose and categorize existing methods. Furthermore, we classify existing methods from the testing objective perspective, covering various components in DBMSs. For representative works, more detailed descriptions are provided to analyze their strengths and limitations. To objectively evaluate the performance of each method, we present an open-source DBMS fuzzing toolkit, OpenDBFuzz. Based on this toolkit, we conduct a detailed experimental comparative analysis of existing methods and finally discuss future research directions.Comment: 34 pages, 22 figure

    Future challenges and recommendations

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    Rapid advances in information technology and telecommunications, and in particular mobile and wireless communications, converge towards the emergence of a new type of “infostructure” that has the potential of supporting a large spectrum of advanced services for healthcare and health. Currently the ICT community produces a great effort to drill down from the vision and the promises of wireless and mobile technologies and provide practical application solutions. Research and development include data gathering and omni-directional transfer of vital information, integration of human machine interface technology into handheld devices and personal applications, security and interoperability of date and integration with hospital legacy systems and electronic patient record. The ongoing evolution of wireless technology and mobile device capabilities is changing the way healthcare providers interact with information technologies. The growth and acceptance of mobile information technology at the point of care, coupled with the promise and convenience of data on demand, creates opportunities for enhanced patient care and safety. The developments presented in this section demonstrate clearly the innovation aspects and trends towards user oriented applications

    DATA_SPHERE

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    This paper presents a comprehensive overview of Database Management Systems (DBMS) and their significance in modern information management. DBMS technology plays a crucial role in the storage, organisation, retrieval, and manipulation of vast amounts of data in various domains, ranging from business operations to scientific research. This abstract highlights the key aspects covered in the paper, including the evolution of DBMS, its architectural components, and the challenges and advancements in the field. The paper begins by discussing the historical development of DBMS, tracing its origins from file-based systems to the emergence of relational databases and the subsequent rise of object-oriented and NoSQL databases. We explore the motivations behind these advancements and their impact on data management. Next, we delve into the fundamental architectural components of a DBMS. We examine the storage layer, which encompasses data structures and access methods, and discuss different indexing techniques for efficient data retrieval. The query processing and optimization module are explored, focusing on query execution plans and cost-based optimization strategies. Additionally, we analyse the transaction management component, highlighting concepts such as ACID properties, concurrency control, and recovery mechanisms. The abstract also highlights the challenges faced by modern DBMS. With the proliferation of big data and the advent of cloud computing, scalability, availability, and performance have become critical concerns. We examine techniques such as parallel and distributed databases, replication, and sharding to address these challenges. Furthermore, we discuss the integration of DBMS with emerging technologies like machine learning and blockchain to leverage their capabilities in data analytics and secure data transactions. Lastly, the abstract touches upon recent advancements in DBMS, including the rise of graph databases for managing interconnected data, the adoption of in-memory databases for high-performance applications, and the exploration of new database models to handle unstructured and semi-structured data. In conclusion, this paper provides a comprehensive overview of DBMS, covering its historical evolution, architectural components, challenges, and recent advancements. By understanding the principles and advancements in DBMS, researchers and practitioners can effectively harness the power of data management systems to tackle the complexities of modern data-driven applications

    Handling Confidential Data on the Untrusted Cloud: An Agent-based Approach

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    Cloud computing allows shared computer and storage facilities to be used by a multitude of clients. While cloud management is centralized, the information resides in the cloud and information sharing can be implemented via off-the-shelf techniques for multiuser databases. Users, however, are very diffident for not having full control over their sensitive data. Untrusted database-as-a-server techniques are neither readily extendable to the cloud environment nor easily understandable by non-technical users. To solve this problem, we present an approach where agents share reserved data in a secure manner by the use of simple grant-and-revoke permissions on shared data.Comment: 7 pages, 9 figures, Cloud Computing 201

    MulTe: A Multi-Tenancy Database Benchmark Framework

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    Multi-tenancy in relational databases has been a topic of interest for a couple of years. On the one hand, ever increasing capabilities and capacities of modern hardware easily allow for multiple database applications to share one system. On the other hand, cloud computing leads to outsourcing of many applications to service architectures, which in turn leads to offerings for relational databases in the cloud, as well. The ability to benchmark multi-tenancy database systems (MT-DBMSs) is imperative to evaluate and compare systems and helps to reveal otherwise unnoticed shortcomings. With several tenants sharing a MT-DBMS, a benchmark is considerably different compared to classic database benchmarks and calls for new benchmarking methods and performance metrics. Unfortunately, there is no single, well-accepted multi-tenancy benchmark for MT-DBMSs available and few efforts have been made regarding the methodology and general tooling of the process. We propose a method to benchmark MT-DBMSs and provide a framework for building such benchmarks. To support the cumbersome process of defining and generating tenants, loading and querying their data, and analyzing the results we propose and provide MULTE, an open-source framework that helps with all these steps

    An Intelligent Market: Possibilities of a Revolution in Supermarkets Organization Using Agent Based Systems

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    This work is focused on studying the possibilities of a new model in business management, integrating the best solutions of IT in the supermarket enterprises. We aim to show how an agent based system is used to manage successfully a market and why it is considered an efficient method to support the challenges of the supermarket enterprises. In a highly competitive environment, the impact of this phenomenon is visible, noting the increase of the interest for electronic systems which offer intelligent services in some activities as administration, marketing, business services, etc. We think agent based systems are the best choice as tools that can automate the analysis of the database information and locate the real useful things

    A development framework for artificial intelligence based distributed operations support systems

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    Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself
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