Database testing

Abstract: Publicly accessible databases are an indispensable resource for retrieving up-to-date information. But they also pose a significant risk to the privacy of the user, since a curious database operator can follow the user's queries and infer what the user is after. Indeed, in cases where the users' intentions are to be kept secret, users are often cautious about accessing the database. It can be shown that when accessing a single database, to completely guarantee the privacy of the user, the whole database should be down-loaded; namely n bits should be communicated (where n is the number of bits in the database).In this work, we investigate whether by replicating the database, more efficient solutions to the private retrieval problem can be obtained. We describe schemes that enable a user to access k replicated copies of a database (k≥2) and privately retrieve information stored in the database. This means that each individual server (holding a replicated copy of the database) gets no information on the identity of the item retrieved by the user. Our schemes use the replication to gain substantial saving. In particular, we present a two-server scheme with communication complexity O(n1/3).

Abstract: Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate the problem: In order to manipulate large sets of complex objects as efficiently as today's database systems manipulate simple records, query-processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and postrelational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set-matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

Abstract: The success of these systems refutes a 1983 paper predicting the demise of database machines [3]. Ten years ago the future of highly parallel database machines seemed gloomy, even to their staunchest advocates. Most database machine research had focused on specialized, often trendy, hardware such as CCD memories, bubble memories, head-per-track disks, and optical disks. None of these technologies fulfilled their promises; so there was a sense that conventional CPUs , electronic RAM, and mcving-head magnetic disks would dominate the scene for many years to come. At that time, disk throughput was predicted to double while processor speeds were predicted to increase by much larger factors. Consequently , critics predicted that multiprocessor systems would scxm be I/O limited unless a solution to the I/O bottleneck was found. Whiie these predictions were fairly accurate about the future of hardware, the critics were certainly wrong about the overall future of parallel database systems. Over the last decade 'Eradata, Tandem, and a host of startup companies have successfully developed and marketed highly parallel machines.

Abstract: A method and system for synchronizing a main database of a server and a local database of a handheld device. A user can use an application residing in the handheld device to make transactions in the local database. During a synchronization operation, the handheld device and server are coupled. The system then determines whether the application should be updated and, if so, causes the server to provide an update. The system also causes the handheld device to provide to the server information related to the transactions made by the user to the local database. The system causes the server to perform transactions on the main database based on the transaction information. The system also causes the server to extract data from the main database. The server can then provide at least some of the extracted data to the handheld device to update the local database.