Decision Support for Distributed Database Fragmentation and Allocation Schema Design

If peer to peer distributed database systems meet current expectations, they are likely to replace virtually all centralized database systems over the next decade. One impediment to the proliferation of peer to peer distributed database systems is the lack of proven and established normative methodologies for designing distributed database fragmentation and allocation schemas. The literature discussed here serves as the basis of research-in-progress for designing, implementing, and empirically evaluating a support system to aid distributed database fragmentation and allocation schema decision making. Future manuscripts are planned that describe the prototype decision support system and our empirically-based experiences

Notopedia Self Learning Study Material BIBLIOPHILY

This project aims at addressing the formal system of students academic, technical activities and notices management system by implementation of an online system for these purposes. Colleges follow a manual system to provide students with their notices, notes and other important documents that are needed . This current system provides students with the bibliophilic data by means of printed notes and documentation. Notices are provided on a notice board which is accessible to students in college premises only. This current analogue system requires a lot of paperwork and cost to provide students with the basic documentation like notes and question papers. Also this analogue system fails at providing the students the flexibility required in terms of time and location, to access these documentation. The concept of notice board i n the college premises is restricted by strictly location requiring students to travel for get ting access to the notices Displayed for them. Moreover this analogue technique is responsible for consumption of more time than is really required for such a short process. Development of this system ensures at addressing these drawbacks of the current sy stem of flexibility and time management. This system uses a web based approach to address and solve these issues by creating an online management system to manage the notes, notices, question papers and student data, unlike formal system. Al so databases ha ve been kept distributed so as to increase the security of data in the system

A threshold based dynamic data allocation algorithm - a Markov Chain model approach

In this study, a new dynamic data allocation algorithm for non-replicated Distributed Database Systems (DDS), namely the threshold algorithm, is formulated and proposed. The threshold algorithm reallocates data with respect to changing data access patterns. The proposed algorithm is distributed in the sense that each node autonomously decides whether to transfer the ownership of a fragment in DDS to another node or not. The transfer decision depends on the past accesses of the fragment. Each fragment continuously migrates ftom the node where it is not accessed locally more than a certain number of past accesses, namely a threshold value. The threshold algorithm is modeled for a fragment of the database as a finite Markov chain with constant node access probabilities. In the model, a special case, where all nodes have equal access probabilities except one with a different access probability, is analyzed. It has been shown that for positive threshold values the fragment will tend to remain at the node with the higher access probability. It is also shown that the greater the threshold values are, the greater the tendency of the fragment to remain at the node with higher access probability will be. The threshold algorithm is especially suitable for a DDS where data access pattern changes dynamically

A Methodology for Vertically Partitioning in a Multi-Relation Database Environment

Vertical partitioning, in which attributes of a relation are assigned to partitions, is aimed at improving database performance. We extend previous research that is based on a single relation to multi-relation database environment, by including referential integrity constraints, access time based heuristic, and a comprehensive cost model that considers most transaction types including updates and joins. The algorithm was applied to a real-world insurance CLAIMS database. Simulation experiments were conducted and the results show a performance improvement of 36% to 65% over unpartitioned case. Application of our method for small databases resulted in partitioning schemes that are comparable to optimal.Facultad de Informátic

A Methodology for Vertically Partitioning in a Multi-Relation Database Environment

Vertical partitioning, in which attributes of a relation are assigned to partitions, is aimed at improving database performance. We extend previous research that is based on a single relation to multi-relation database environment, by including referential integrity constraints, access time based heuristic, and a comprehensive cost model that considers most transaction types including updates and joins. The algorithm was applied to a real-world insurance CLAIMS database. Simulation experiments were conducted and the results show a performance improvement of 36% to 65% over unpartitioned case. Application of our method for small databases resulted in partitioning schemes that are comparable to optimal.Facultad de Informátic

Low overhead concurrency control for partitioned main memory databases

Database partitioning is a technique for improving the performance of distributed OLTP databases, since "single partition" transactions that access data on one partition do not need coordination with other partitions. For workloads that are amenable to partitioning, some argue that transactions should be executed serially on each partition without any concurrency at all. This strategy makes sense for a main memory database where there are no disk or user stalls, since the CPU can be fully utilized and the overhead of traditional concurrency control, such as two-phase locking, can be avoided. Unfortunately, many OLTP applications have some transactions which access multiple partitions. This introduces network stalls in order to coordinate distributed transactions, which will limit the performance of a database that does not allow concurrency. In this paper, we compare two low overhead concurrency control schemes that allow partitions to work on other transactions during network stalls, yet have little cost in the common case when concurrency is not needed. The first is a light-weight locking scheme, and the second is an even lighter-weight type of speculative concurrency control that avoids the overhead of tracking reads and writes, but sometimes performs work that eventually must be undone. We quantify the range of workloads over which each technique is beneficial, showing that speculative concurrency control generally outperforms locking as long as there are few aborts or few distributed transactions that involve multiple rounds of communication. On a modified TPC-C benchmark, speculative concurrency control can improve throughput relative to the other schemes by up to a factor of two.National Science Foundation (U.S.). (Grant number IIS-0704424)National Science Foundation (U.S.). (Grant number IIS-0845643

A Methodology for Vertically Partitioning in a Multi-Relation Database Environment

Vertical partitioning, in which attributes of a relation are assigned to partitions, is aimed at improving database performance. We extend previous research that is based on a single relation to multi-relation database environment, by including referential integrity constraints, access time based heuristic, and a comprehensive cost model that considers most transaction types including updates and joins. The algorithm was applied to a real-world insurance CLAIMS database. Simulation experiments were conducted and the results show a performance improvement of 36% to 65% over unpartitioned case. Application of our method for small databases resulted in partitioning schemes that are comparable to optimal.Facultad de Informátic

Reengineering of Legacy Systems to Distributed Environments.

The object-oriented paradigm and client/server and distributed technologies have become widely used in the last decade. There is an increasing interest to migrate and reengineer legacy systems to these new hardware technologies and software development paradigms. Software engineers who wish to reengineer such legacy systems face challenges, such as lack of documentation and programs that are difficult to comprehend. Middleware technologies such as CORBA and DCOM make the development of new distributed systems, as well as the migration of legacy systems to distributed platforms, more feasible. Distribution of a system consists of two parts: (1) subsystem decomposition and (2) allocation of the subsystems to different sites. In this research, we define a reengineering environment that assists with the migration of legacy systems to distributed environments. We define a reengineering methodology that uses reverse engineering, software metrics, clustering, and data mining to migrate legacy systems to object-based distributed environments. The reengineering environment includes the methodology and an integrated set of tools that support the implementation of the methodology. The methodology consists of multiple phases. First, we use reverse engineering techniques for program comprehension and design recovery. We then decompose the system into a hierarchy of subsystems by defining relationships between the entities of the underlying paradigm of the legacy system. The decomposition is driven by data mining, software metrics, and clustering techniques. Next, if the underlying paradigm of the legacy system is not object-based, we perform object-based adaptations on the subsystems. We then create components by wrapping objects and defining an interface. Finally, we allocate components to different sites by specifying the requirements of the system and characteristics of the network as an integer-programming model that minimizes the remote communication. We use middleware technologies for the implementation of the distributed object system

Workload Matters: A Robust Approach to Physical RDF Database Design

Recent advances in Information Extraction, Linked Data Management and the Semantic Web have led to a rapid increase in both the volume and the variety of publicly available graph-structured data. As more and more businesses start to capitalize on graph-structured data, data management systems are being exposed to workloads that are far more diverse and dynamic than what they were designed to handle. In particular, most systems rely on a workload-oblivious physical layout with a fixed-schema and are adaptive only if the changes in the schema are minor. Thus, they are unable to perform consistently well across different types of workloads. This thesis introduces fundamental techniques for supporting diverse and dynamic workloads in RDF data management systems. Instead of assuming anything about the workload upfront, these techniques allow systems to adjust their physical designs as queries are executed. This includes changing the way (i) records are clustered in the storage system, (ii) data are organized and indexed, and (iii) queries are optimized, all at runtime. The thesis proceeds with a discussion of the challenges that have been encountered in implementing these ideas in a proof-of-concept prototype called chameleon-db, and it concludes with a thorough experimental evaluation