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We study the problem of privately performing database queries i.e., keyword searches and conjunctions over them, where a server provides its own database for client query-based access. We propose a cryptographic model for the study of such protocols,by expanding previous well-studied models of keyword search and private information retrieval to incorporate a more practical data model: a time-varying, multi-attribute and multiple-occurrence database table. Our first result is a 2-party private database retrieval protocol. Like all previous work in private information retrieval and keyword search, this protocol still satisfies server time complexity linear in the database size. Our main result is a private database retrieval protocol in a 3-party model where encrypted data is outsourced to a third party i.e., a cloud server, satisfying highly desirable privacy and efficiency properties; most notably: 1 no unintended information is leaked to clients or servers, and only minimal 'access pattern' information is leaked to the third party; 2 for each query, all parties run in time only logarithmic in the number of database records; 3 the protocol's runtime is practical for real-life applications, as shown in our implementation where we achieve response time that is only a small constant slower than commercial non-private protocols like MySQL.

We present a database that can serve as a tool for tuning and evaluation of miscellaneous program analysis tools. The database contains bug-reports produced by various tools applied to various source codes. The bug-reports are classified as either real errors or false positives. The database currently contains more than 800 bug-reports detected in the Linux kernel 2.6.28. Support of other software projects written in various programming languages is planned. The database can be downloaded and manipulated by SQL queries, or accessed via a web frontend.

We present an approach for data encoding and recovery of lost information in a distributed database system. The dependencies between the informational redundancy of the database and its recovery rate are investigated, and fast recovery algorithms are developed.

Interaction is a typical phenomenon in database systems. It involves several components of DBMS: the data, the queries, the optimization techniques and devices. Each component is a critical issue for the database performance. The interaction between queries is well established and recognized by the database community. The interaction among optimization techniques has been also exploited during the physical design of databases. The interaction in multi levels is usually ignored when selecting optimization techniques. In our work, we deal with the combined problem of query scheduling, buffer management and horizontal partitioning simultaneously, by proposing an interaction-aware solution. An experimental study is given to show the efficiency of our proposal.

In order to store and manage the basic information and inspection task for overhead high voltage power transmission line inspection robots, a database system is built up. This database system can store and manage such information as sensor information, historical data, report, inspection task, and line fault detected. Moreover, there is a simple line fault expert database in the database system developed for automatic line fault detection of the robots. SQLite is adopted as the backend database in this system. According to the specific requirements of the actual inspection application, the E-R model of the database system is designed, which is composed of two parts: sensor data model and line inspection model. Application experiments reveal that this database system can accomplish the information storage and management work for inspection robots.

The TPC formed a subcommittee in 2010 to develop TPC V, a benchmark for virtualized databases. We soon discovered two major issues. First, a database benchmark running in a VM, or even a consolidation scenario of a few database VMs, is no longer adequate. There is demand for a benchmark that emulates cloud computing, e.g., a mix of heterogeneous VMs, and dynamic load elasticity for each VM. Secondly, waiting for system or database vendors to develop benchmarking kits to run such a benchmark is problematic. Hence, we are developing a publicly-available, end-to-end reference kit that will run against the open source PostgreSQL DBMS. This paper describes TPC V and the proposed architecture of its reference kit; provides a progress report; and presents results from prototyping experiments with the reference kit.

In this paper, we propose to develop a framework to analyze the monetary cost of running database systems in the public cloud. The framework offers guidelines and methodologies in analyzing and estimating monetary cost of database systems. It consists of multiple components including categorizing database performance tuning knobs, benchmarking the price/performance of computation resources offered by the cloud provider, and building a monetary cost model. As a case study of our proposed framework, we conduct an in-depth study on two popular open-source database systems with respect to two cloud providers. We find that evaluating a query spans a wide range of monetary costs (with a difference up to 91%), and the experimental results demonstrate the accuracy of our monetary cost estimation.

Although computer technology and database management technology continue to develop still not to meet the needs of the users of the Web database applications, At the same time, there are serious security risks. this paper introduces the procedure development of the database and architecture of major database system, And to analyze the security risks of Web database application system during operation, the specific measures are proposed to solve these problems.

Array DBMSs extend the set of supported data structures in databases with (potentially large) multi-dimensional arrays. This information category actually comprises a core data structure in many scientific applications. When it comes to Petabyte archives, storage costs prohibit importing (i.e., copying) such data into a database. Therefore, in-situ processing of database queries is required, that is: evaluating queries on the original files, without previous insertion into the database. We have implemented such an in-situ feature for the rasdaman Array DBMS. In this demonstration, we show with rasdaman how query processing in array databases can simultaneously rely on arrays stored in the database -- as usual -- and in operating system files, like preexisting archives.

TPC Benchmarks have become the gold standard in database benchmarks. The Companies who publish TPC Benchmarks have a significant investment in the workload, benchmark implementation and publication requirements. We will explore ideas on how TPC Benchmarks with limited modification can be used to characterize database performance in a cloud environment. This is a natural progression beyond the current TPC-VMS Specification that leverages existing TPC Benchmarks to measure database performance in a virtualized environment. The TPC-VMS Specification only addresses the consolidation of multiple databases in a virtualized or cloud environment. In addition to consolidation, we will address the cloud characteristics of load balancing, migration, resource elasticity and deployment.

Query processing in a distributed database system requires the transmission of data between sites using communication networks. Distributed query processing is an important factor in the overall performance of a distributed database system. In distributed query optimization, complexity and cost increases with increasing number of relations in the query. Cost is the sum of local cost (I/O cost and CPU cost at each site) and the cost of transferring data between sites. Extensive research has been done for query processing and optimization in distributed databases. Numerous search strategies like static, dynamic and randomized strategies are available for determining an optimal plan. However these search strategies are not suitable for the autonomous distributed database systems. These search strategies make certain assumptions (like all sites have same processing capability), which do not hold for autonomous systems. Mariposa, Query Trading (QT) and Query Trading with Processing Task Trading (QTPT) are the query processing algorithms developed for autonomous distributed database systems. However, they incur high optimization cost due to involvement of all nodes in generating optimal plan. We present our solution k-QTPT, to reduce the high optimization cost incurred by QTPT. In k-QTPT, only k nodes participate in generating optimal plans. We discuss implementation details of QT, QTPT algorithm and our solution k-QTPT. We evaluate k-QTPT through emulation. We show that the cost of optimization reduces substantially in k-QTPT as compared to QT and QTPT.

The main task of product configuration is that according to the rules of the configuration and limits of configuration conditions, checking parts searched products information database from for assembling, mutual constraint, etc, eventually outputting an effective Bill of Materials (BOM). In the paper, it is introduced how to create single-layer BOM in relational database. Based on object-oriented technology of relational database, model of data structure is constructed corresponds to the product configuration system and database design of the product configuration system is implementing. it is an important role for Product Configuration Management System on Mass Customization that the effective management of the product data, making full use of the present resources and rapidly realizing design of customized product, and provides reference for database design of similar system.

Resource-optimization of the infrastructure for service oriented applications require accurate performance models. In this paper we investigate the performance dynamics of a MySQL/InnoDB database server with write-heavy workload. The main objective of our investigation was to understand the system dynamics due to the buffering of disk operations that occurs in database servers with write-heavy workload. In the paper, we characterize the traffic and its periodic anomalies caused by flushing of the buffer. Further, we present a performance model for the response time of the requests and show how this model can be configured to fit with actual database measurements. Also, we show that window-based admission control outperforms rate-based admission control for these types of systems.

Database partitioning is a common technique adopted by database systems running on single or multiple physical machines. It is always crucial yet challenging for a DBA to choose an appropriate partitioning scheme for a database according to a specific query workload. In this paper, we present PEACOD, a platform that aims to ease the burden of DBAs for database partitioning design. By automating the processing of database partitioning scheme evaluation and comparison, PEACOD provides the DBAs with a conventional way to choose a suitable scheme.

Data Warehouses (DWs) are the core of enterprise sensitive data, which makes protecting confidentiality in DWs a critical task. Published research and best practice guides state that encryption is the best way to achieve this and maintain high performance. However, although encryption algorithms strongly fulfill their security purpose, we demonstrate that they introduce massive storage space and response time overheads, which mostly result in unacceptable security-performance tradeoffs, compromising their feasibility in DW environments. In this paper, we enumerate state-of-the-art data masking and encryption solutions and discuss the issues involving their use from a data warehousing perspective. Experimental evaluations using the TPC-H decision support benchmark and a real-world sales DW support our remarks, implemented in Oracle 11g and Microsoft SQL Server 2008. We conclude that the development of alternate solutions specifically tailored for DWs that are able to balance security with performance still remains a challenge and an open research issue.

In order to guarantee the stability and reliability of database performance, database monitoring system and program are necessary. The previous commercial database monitoring servers and programs make database overhead and degrade performance of database management system. And there were no alert message or warning message for database administrators in the previous commercial database monitoring system.

In this paper, we analyse Java source code of embedded database applications by means of static code analysis. If the underlying database schema of such an application is subject to refactoring or database tuning, then the SQL statements in the embedding Java program need to be adapted correspondingly. This should be done mostly automatically, since changing software manually is error-prone and time consuming. For determining the SQL statements that access the database, we can either look at the database logfile, an audit file, or at the Java source code itself. Here, we show how to derive the strings of dynamic SQL statements directly from the Java source code. We do this without using a debugger or a virtual machine technique; instead, we trace the values of variables that contribute to a query string backwards to predict the values of contributing program variables as precisely as possible. We use PROLOG's declarative features and its backtracking mechanism for code analysis, refactoring, and tuning.

Performance of OLAP queries becomes a critical issue as the amount of data in the data warehouses increases rapidly. To solve this performance issue, we proposed a high performance database cluster system called HyperDB in which many PCs can be mobilized for excellent performance. In HyperDB, an OLAP query can be decomposed into sub-queries, and each of the sub-queries can be processed independently on a PC in a short time. But if an OLAP query has nested form (i.e., nested SQL), it could not be decomposed into sub-queries. In this paper, we propose a parallel distributed query processing algorithm for nested queries in HyperDB system. Traditionally, parallel distributed processing of nested queries is known as a difficult problem in the database area.

This article presents the principle about Web database access based on ASP.NET, expounds three kinds of connection methods and two accessing models that about ASP.NET page and database, and then analyzes and compares various means about using ADO.NET technology on database access. In the meantime, provides some utility codes. ASP. NET is currently an ideal choice for visiting Web database.

To protect two party privacy of database’s query, known solutions are encrypt the entire database and thus the encryption cost is excessively high. To resolve this problem, we construct a new efficient solution to database’s query in semi-honest mode, Based on the assumption of combination commutative encryption and the oblivious transfer protocol. The protocol can safely and efficiently finished database’s secure query lack of third-party participation. Furthermore, the correctness, the security and the computational complexity of this scheme are as well analyzed. The results of analysis show that compared with known solutions, the proposed scheme has much less computational complexity.

This study uses XML as a middleware transparent access to heterogeneous databases, and uses the standard database language based on SQL-99 to implement the operation of global data and global query, enabling users to make global data in the system tables are created, deleted, the global data insert, update, delete, and global query and other database operations.

The new characteristics of flash memory bring great challenges in optimizing database performance, by using new querying algorithms, indexes, buffer management schemes, and new transaction processing protocols. In this talk, we will first present an overview on the FlashDB project, which was launched in 2009 and supported by the National Natural Science Foundation of China (No. 60833005). The project aims at constructing the fundamental theory and design principles of flash-based database systems including a series of key problems, such as system architecture, storage management and indexing, query processing, transaction processing, buffer management, etc. In particular, we focus on establishing a basis for data management involving flash memory, developing database management systems for flash-based SSDs, and preparing a test bed for flash-based database applications. During the past two years, we have made some achievements in buffer management [1, 2], index structures [3], storage management [4], and SSD simulation platform [5]. After a brief introduction on the current research results in the project, we will discuss some experiences and lessons concluded from the study. We will emphasize several issues that may be open up exciting avenues and influence the direction of the research within the scope of flash-based database systems.

State of the art 3D simulation applications like virtual testbeds for space robotics, industrial automation or even forest inventory require a highly flexible but still real-time capable data management system. For this, we combine a high-performance internal simulation database with external object-oriented databases into a new real-time capable data management architecture for database-driven 3D simulation systems. To achieve this, we apply well-known database techniques to a 3D simulation system's internal object-oriented data management. Such a simulation database can dynamically adopt completely new data schemata, even at runtime. New simulation applications can then be designed by putting a domain specific schema and the corresponding data into an otherwise "empty" simulation database. To seamlessly combine the two databases we use a flexible interface that synchronizes schema and data.

Andorid which is the popular smart phone OS uses a database system to manage its private data storage. Although the database system supports a powerful and lighteweight database engine, its performance is limited by a single storage media, NAND flash memory, and a single file system, YAFFS2. In this paper, we propose a new embedded database system based on hybrid storage of PRAM and NAND flash memory. Using the byte-level and in-place read/write capability of PRAM, we separately manage a journaling process of the database system. It increases the transaction speed and reduces the additional overhead caused by NAND flash memory. We implement our database system using SQLite and dual file systems (YAFFS2 and PRAMFS). Consequently, the proposed database system reduces the response time of the database transaction by 45% compared to the conventional database system. In addition, it mitigates the burden of NAND flash memory management. Moreover, previous database applications can be executed on the proposed system without any modification.

In recent years, many algorithms for automatic physical database tuning have been proposed and successfully used in tools for administration of relational database management systems. The novel method described in this paper uses a steady-state evolutionary approach to continuously give index recommendations so that the database management system can adapt to changing workload and data distribution. Contrary to online algorithms offering recommendations on a per-query basis, our solution takes into account index reuse accross different queries. The experiments show that the quality of the recommendations obtained by the proposed method matches the quality of recommendations given by the best offline index selection algorithms. Moreover, high performance and low memory footprint of the method make it suitable for autonomic database tuning systems.

The goal of this study is design of a database holding archives of weather forecast systems. The detailed description of the project and analysis of the database performance is presented, along with the experimental performance tests of key algorithms. A standard mode of data access is optimal for a sequential access to individual 2D grids (time-local and spatially-global), whereas new applications require reading long time series of localised data (spatially-local and time-global). The design goal is to increase a performance of access to archival spatially-local-time-global data, without visible degradation of performance of the standard access mode. The database is designed as two separate layers. The Format Translation Layer (FTL) is an interface between the database and the file-based output of the simulation and analysis programs. The Distributed Data Storage Layer (DDSL) is responsible for a secure data storage and an efficient access. The FTL reads output forecasts and converts them to the spatially local format. It splits large 2D arrays of data into small patches and forms 3D arrays, using time as the third dimension. The theoretical analysis of the performance shows that four orders of magnitudes improvement in comparison with the standard serial access and two orders of magnitude in comparison with parallelised version of the spatially-global access can be achieved.

When a forensic investigation is carried out in the enterprise environment, most of the important data is stored in database servers, and data stored in them are very important elements for a forensic investigation. As for database servers with such data stored, there are over 10 various kinds, such as SQL Server, Mysql and Oracle. All the methods of investigating a database system are important, but this study suggests a single methodology likely to investigate all the database systems while considering the common characteristics of database system. A method of detecting a server, data acquiring and investigating data in the server can be usefully used for such an investigation in the enterprise environment. Therefore, such a methodology will be explained through a way of carrying out a forensic investigation on SQL Server Database of Microsoft Corporation.

The natural and surrogate key architectures are two competing approaches to specifying keys in relational databases. We analyze the performance of these approaches with respect to the update complexity and the query data and expression complexity. Our results provide new insights into the advantages and disadvantages of both approaches.

In this study, a fuzzy query tool (SQLf) for non-fuzzy database management systems was developed In addition, samples of fuzzy queries were made by using real data with the tool developed in this study Performance of SQLf was tested with the data about the Marmara University students' food grant The food grant data were collected in MySQL database by using a form which had been filled on the web The students filled a form on the web to describe their social and economical conditions for the food grant request This form consists of questions which have fuzzy and crisp answers The main purpose of this fuzzy query is to determine the students who deserve the grant The SQLf easily found the eligible students for the grant through predefined fuzzy values The fuzzy query tool (SQLf) could be used easily with other database system like ORACLE and SQL server.

Embedded database technology can be used to manage MPEG-7 data with limited resources. In that case, we need a clustering method for the efficient storage of MPEG-7 documents. The present study designed and implemented a MPEG-7 document management system that can store MPEG-7 documents efficiently in mobile terminals such as PDA. The system used Berkeley DB XML, an embedded XML database system based on MPEG-7 data clustering.

This chapter illustrates how the concepts and algorithms described earlier in this book can be used to build practical database replication systems. This is achieved first by addressing architectural challenges on how required functionality is provided by generally available software componentes and then how different components can be efficiently integrated. A second set of practical challenges arises from experience on how performance assumptions map to actual environments and real workloads. The result is a generic architecture for replicated database management systems, focusing on the interfaces between key components, and then on how different algorithmic and practical optimization options map to real world gains. This shows how consistent database replication is achievable in the current state of the art.

The insider threat against database management systems is a dangerous security problem. Authorized users may abuse legitimate privileges to masquerade as other users or to maliciously harvest data. We propose a new direction to address this problem. We model users' access patterns by profiling the data points that users access, in contrast to analyzing the query expressions in prior approaches. Our data-centric approach is based on the key observation that query syntax alone is a poor discriminator of user intent, which is much better rendered by what is accessed. We present a feature-extraction method to model users' access patterns. Statistical learning algorithms are trained and tested using data from a real Graduate Admission database. Experimental results indicate that the technique is very effective, accurate, and is promising in complementing existing database security solutions. Practical performance issues are also addressed.

Database is important to ensure the data can be store, update and retrieve back. The famous data modelling using the Entity Relationship Model already introduced more than thirty years. However, designing a good database is still a big issue especially in designing optimum database because it is very hard to do consistency checking between system design and database design to fulfil user needs. In this paper, we propose an alternative method for designing optimum database system based on object oriented methodology. The schema table is extracted from class diagram and then the schema will be compared with the user interface to normalize the structure. Data sanitization is introduced to remove the unused attributes to provide final schema table.

A large body of research concerns the adaptability of database systems. Many commercial systems already contain autonomic processes that adapt configurations as well as data structures and data organization. Yet there is virtually no possibility for a just measurement of the quality of such optimizations. While standard benchmarks have been developed that simulate real-world database applications very precisely, none of them considers variations in workloads produced by human factors. Today's benchmarks test the performance of database systems by measuring peak performance on homogeneous request streams. Nevertheless, in systems with user interaction access patterns are constantly shifting. We present a benchmark that simulates a web information system with interaction of large user groups. It is based on the analysis of a real online eLearning management system with 15,000 users. The benchmark considers the temporal dependency of user interaction. Main focus is to measure the adaptability of a database management system according to shifting workloads. We will give details on our design approach that uses sophisticated pattern analysis and data mining techniques.

Data center consolidation, for power and space conservation, has driven the steady development and adoption of virtualization technologies. This in turn has lead to customer demands for better metrics to compare virtualization technologies. The technology industry has responded with standardized methods and measures for benchmarking hardware and software performance with virtualization. This paper compares the virtualization technologies available today and existing benchmarks to measure them. We describe some real-life data center scenarios that are not addressed by current benchmarks and highlight the need for virtualization workloads that incorporate database-heavy computing needs. We present data from experiments running existing TPC database workloads in a virtualized environment and demonstrate that virtualization technologies are available today to meet the demands of the most resource---intensive database application. We conclude with ideas to the TPC for a benchmark that can effectively measure database performance in a virtual environment.

Scaling up web applications requires distribution of load across multiple application servers and across multiple database servers. Distributing load across multiple application servers is fairly straightforward; however distributing load (select and UDI queries) across multiple database servers is more complex because of the synchronization requirements for multiple copies of the data. Different techniques have been investigated for data placement across multiple database servers, such as replication, partitioning and de-normalization. In this paper, we describe our architecture that utilizes these data placement techniques for determining the best possible layout of data. Our solution is general, and other data placement techniques can be integrated within our system. Once the data is laid out on the different database servers, our efficient query router routes the queries to the appropriate database server/(s). Our query router maintains multiple connections for a database server so that many queries are executed simultaneously on a database server, thus increasing the utilization of each database server. Our query router also implements a locking mechanism to ensure that the queries on a database server are executed in order. We have implemented our solutions in our system, that we call SIPD (System for Intelligent Placement of Data). Preliminary experimental results illustrate the significant performance benefits achievable by our system.

Many large-scale online services use structured storage to persist metadata and sometimes data. The structured storage is typically provided by standard database servers such as Microsoft's SQL Server. It is important to understand the workloads seen by these servers, both for provisioning server hardware as well as to exploit opportunities for energy savings and server consolidation. In this paper we analyze disk I/O traces from production servers in four internet services as well as servers running TPC benchmarks. We show using a range of load metrics that the services differ substantially from each other and from standard TPC benchmarks. Online services also show significant diurnal patterns in load that can be exploited for energy savings or consolidation. We argue that TPC benchmarks do not capture these important characteristics and argue for developing benchmarks that can be parameterized with workload features extracted from live production workload traces.

Database systems have traditionally used a Client-Server architecture. As the server becomes overloaded, clients experience an increase in query response time, and in the worst case the server may be unable to provide any service at all. In file-sharing, the problem of server overloading has been addressed by the use of Peer-to-Peer (P2P) techniques in which users (peers) supply files to each other, so sharing the load. This paper describes the Wigan P2P Database System, which was designed to investigate if P2P techniques for reducing server load, thus increasing system scalability, could be applied successfully in a database environment. It is based on the BitTorrent file-sharing approach. This paper introduces the Wigan system architecture, explaining how the BitTorrent approach must be modified for a P2P database server. It presents and analyses experimental results, including the TPC-H benchmark, which show that the approach can succeed in delivering scalability in particular cases.

Set to replace the aging TPC-C, the TPC Benchmark E is the next generation OLTP benchmark, which more accurately models client database usage. TPC-E addresses the shortcomings of TPC-C. It has a much more complex workload, requires the use of RAID-protected storage, generates much less I/O, and is much cheaper and easier to set up, run, and audit. After a period of overlap, it is expected that TPC-E will become the de facto OLTP benchmark.

The demand of analyzing and processing mass data is increasing in recent years. Though several optimization versions developed, the traditional RDBMS still met a lot of difficulties when facing so huge volume of data. A newly designed distributed structured database HugeTable is proposed, which have the advantage of supporting very large scale of data and fast query speed. HugeTable also have a good compatibility with the standard SQL query language. The basic functions, system architecture and critical techniques are discussed in detail. The usability and efficiency are proved by experiments.

Reflective Database Access Control (RDBAC) is a model in which a database privilege is expressed as a database query itself, rather than as a static privilege in an access control matrix. RDBAC aids the management of database access controls by improving the expressiveness of policies. The Transaction Datalog language provides a powerful syntax and semantics for expressing RDBAC policies, however there is no efficient implementation of this language for practical database systems. We demonstrate a strategy for compiling policies in Transaction Datalog into standard SQL views that enforce the policies, including overcoming significant differences in semantics between the languages in handling side-effects and evaluation order. We also report the results of evaluating the performance of these views compared to policies enforced by access control matrices. This implementation demonstrates the practical feasibility of RDBAC, and suggests a rich field of further research.

Load balancing architectures can be used to solve overloading problems on web sites. Recently, most of the contents of web pages are retrieved from a database. Thus, the arrangement of the database server in a load balancing architecture is one of the most important factors that impact the overall performance. In this paper, we analyze the performance of load balancing architectures with a centralized database arrangement and a decentralized database arrangement. With a series of experiments, we can find the most appropriate arrangement for the database server. The experimental results show that when the number of client requests is small, using the decentralized arrangement results in a lower average response time because no network communications are needed. But when the number of client requests is large, using the centralized database architecture can achieve higher performance because the database server can share the load of web servers.

The purpose of this talk is to provide a comprehensive state of the art concerning the evolution of query optimization methods from centralized database systems to data Grid systems through parallel, distributed and data integration systems. For each environment, we try to describe synthetically some methods, and point out their main characteristics.

One of the most difficult tasks in the design of information systems is how to control the behaviour of the back-end storage engine, usually a relational database. As the load on the database increases, the longer issued transactions will take to execute, mainly because the presence of a high number of locks required to provide isolation and concurrency. In this paper we present MIDAS, a middleware designed to manage the behaviour of database servers, focusing primarily on guaranteeing transaction execution within an specified amount of time (deadline). MIDAS was developed for Java applications that connects to storage engines through JDBC. It provides a transparent QoS layer and can be adopted with very few code modifications. All transactions issued by the application are captured, forcing them to pass through an Admission Control (AC) mechanism. To accomplish such QoS constraints, we propose a novel AC strategy, called 2-Phase Admission Control (2PAC), that minimizes the amount of transactions that exceed the established maximum time by accepting only those transactions that are not expected to miss their deadlines. We also implemented an enhancement over 2PAC, called diffserv – which gives priority to small transactions and can adopted when their occurrences are not often.

We introduce AdaptPD, an automated physical design tool that improves database performance by continuously monitoring changes in the workload and adapting the physical design to suit the incoming workload. Current physical design tools are offline and require specification of a representative workload. AdaptPD is “always on” and incorporates online algorithms which profile the incoming workload to calculate the relative benefit of transitioning to an alternative design. Efficient query and transition cost estimation modules allow AdaptPD to quickly decide between various design configurations. We evaluate AdaptPD with the SkyServer Astronomy database using queries submitted by SkyServer’s users. Experiments show that AdaptPD adapts to changes in the workload, improves query performance substantially over offline tools, and introduces minor computational overhead.

Sensor database is a type of database management system which offers sensor data and stored data in its data model and query languages. In this system, when a user poses a query to this sensor database, the query will be disseminated across the database. During this process, each sensor generates data that match the query from its covered area and then returns the data to the original sensor. In order to achieve an energy-efficient implementation, it will be useful to select a minimally sufficient subset of sensors to keep active at any given time. Thus, how to find a subset efficiently is an important problem for sensor database system. We define this problem as sensor database coverage (SDC) problem. In this paper, we reduce the SDC problem to connected set cover problem, then present two approximation algorithms to select a minimum connected set cover for a given sensor database. Moreover, to guarantee robustness and accuracy, we require a fault-tolerant sensor database, which means that each target in a query region will be covered by at least m sensors, and the selected sensors will form a k -connected subgraph. We name this problem as (k ,m )-SDC problem and design another approximation algorithm. These three algorithms are the first approximation algorithms with guaranteed approximation ratios to SDC problem. We also provide simulations to evaluate the performance of our algorithms. We compare the results with algorithms in [17]. The comparison proves the efficiency of our approximations. Thus, our algorithms will become a new efficient approach to solve coverage problem in sensor database systems.

Data mining algorithms are often embedded in more complex systems, serving as the provider of data for internal decision making within these systems. In this paper we address an interesting problem of using data mining techniques for database query optimization. We introduce the concept of conditional cardinality patterns and design an algorithm to compute the required values for a given database schema. However applicable to any database system, our solution is best suited for data warehouse environments due to the special characteristics of both database schemata being used and queries being asked. We verify our proposal experimentally by running our algorithm against the state-of-the-art database query optimizer. The results of conducted experiments show that our algorithm outperforms traditional cost-based query optimizer with respect to the accuracy of cardinality estimation for a wide range of queries.