B. M. Monjurul Alom

Deadlock Detection Views of Distributed Database

Deadlock detection is very difficult in a distributed database system because no controller has complete and current information about the system and data dependencies. The deadlock problem is intrinsic to a distributed database system which employs locking as its concurrency control algorithm. This paper attempts a comprehensive study of deadlock detection in distributed database systems. Afterwards, a deadlock detection algorithm is presented. The algorithm is based on creating Linear Transaction Structure (LTS), Distributed Transaction Structure (DTS), finding local and global cycle, deciding priority Id of the transaction and local-global abortion. The proposed algorithm does not detect any false deadlock or exclude any really existing deadlocks. In this technique global deadlock is not dependent on the local deadlock.

Optimization of Detected Deadlock Views of Distributed Database

Deadlock is one of the most serious problems in multitasking concurrent programming systems. The deadlock problem becomes further complicated when the underlying system is distributed and when tasks have timing constraints. Deadlock detection and optimization is very difficult in a distributed database system because no controller has complete and current information about the system and data dependencies. The deadlock problem is intrinsic to a distributed database system which employs locking as its concurrency control algorithm. In this paper, an optimization technique for the detected deadlock is presented which minimizes the abortion of the selected victim transactions. The optimization technique is concerned with the detection of the transactions which are the basis for the most of the deadlock cycles (either local or global) in the system. The presented technique aborts the transaction’s requests which are really to blame for the formation of many deadlock cycles. Also the presented deadlock detection algorithm does not detect any false deadlock or exclude any really existing deadlocks. In this technique global deadlock is not dependent on the local deadlock system

Querying Semistructured Data with Compression in Distributed Environments

As data management applications grow more complex, they are likely to need efficient distributed query processing. In Distributed Database Systems complete replication consists of maintaining complete copies of the database at each site; this has advantages such as highest locality of reference, highest reliability, availability, and is best for reading. The most promising and dominant data format for data processing and representing on the Internet is the semistructured data form termed XML. XML data has no fixed schema; it evolved and is self describing which results in management difficulties compared to, for example relational data. It is therefore a major challenge for the database community to design query languages and storage methods that can retrieve semistructured data. In this paper, we present a storing and querying scheme for semistructured data views of relational form in distributed environments. The proposed technique stores path dictionary, word dictionary, attribute dictionary, and the complete compressed replication of semistructured data in each distributed site of the DDBS. The presented technique provides query performance improvement due to the compression of semistructured data.

DRXQP: A Dynamic Relational XML Query Processor

The most promising and dominant data format for data processing and representation on the Internet is the semistructured data form termed XML. XML data has no fixed schema; it evolved and is self describing which results in management difficulties compared to, for example, relational data. It is therefore a major challenge for the database community to design query languages and storage methods that can retrieve semistructured data efficiently. In this paper, we present a querying scheme for semistructured data views of relational form. The proposed technique stores element-paths, attributes, contents of the element paths and attributes, and XML processing instructions in a dynamic relational structure termed as Multi-XML-Data-Structure (MXDS). We also analyse relational query processing time and XML query processing time to compare the query performance. The presented technique supports different types of XML query.

Single Vector Large Data Cardinality Structure to Handle Compressed Database in a Distributed Environment

Loss-less data compression is attractive in database systems as it may facilitate query performance improvement and storage reduction. Although there are many compression techniques that handle the whole database in main memory, problems arise when the amount of data increases gradually over time, and also when the data has high cardinality. Management of a rapidly evolving large volume of data in a scalable way is very challenging. This paper describes a disk based single vector large data cardinality approach, incorporating data compression in a distributed environment. The approach provides substantial storage performance improvement compared to other high performance database systems. The presented compressed database structure provides direct addressability in a distributed environment, thereby reducing retrieval latency when handling large volumes of data.