Inference Optimization using Relational Algebra

Exact inference procedures in Bayesian networks can be expressed using relational algebra; this provides a common ground for optimizations from the AI and database communities. Specifically, the ability to accomodate sparse representations of probability distributions opens up the way to optimize for their cardinality instead of the dimensionality; we apply this in a sensor data model.\u

Analyatical Study and Evaluation of Database Optimization Techniques

Data Management can be considered as one of most crucial part in world of technology. The primary challenge is not just maintaining data & information, but to access them in an efficient manner as well. Database optimization techniques have been designed to address these tasks that may otherwise limit the performance of a database to an extent of vulnerability. In this paper we discuss the aspects of performance optimization related to data access in transactional databases. Furthermore, we analyze the effect of these optimization techniques. One of key part in database management is database management tools they provide query optimizer that helps to find out the efficient method for SQL query statement to access requested data by user. This optimizer chooses the plan with the lowest cost among all considered candidate plans. Optimizer uses available statistics to calculate cost, for a specific query in a given environment, the cost computation accounts for factors of query execution such as I/O, CPU, and communication

Main Memory Implementations for Binary Grouping

An increasing number of applications depend on efficient storage and analysis features for XML data. Hence, query optimization and efficient evaluation techniques for the emerging XQuery standard become more and more important. Many XQuery queries require nested expressions. Unnesting them often introduces binary grouping. We introduce several algorithms implementing binary grouping and analyze their time and space complexity. Experiments demonstrate their performance

Dynamic Programming: The Next Step

Since 2013, dynamic programming (DP)-based plan generators are capable of correctly reordering not only inner joins, but also outer joins. Now, we consider the next big step: reordering not only joins, but also joins and grouping. Since only reorderings of grouping with inner joins are known, we first develop equivalences which allow reordering of grouping with outer joins. Then, we show how to extend a state-of-the-art DP-based plan generator to fully explore these new plan alternatives

An evaluation between Bloom Filter join and PERF join in Distributed Query Processing

Nowadays, with the explosion of information and the telecommunication era\u27s coming, more and more huge applications encourage decentralization of data while accessing data from different sites [HFB00]. The process of retrieving data from different sites called Distributed Query Processing. The objective of distributed query optimization is to find the most cost-effective of executing query across the network [OV99]. Semijoin [BC81] [BG+81] is known as an effective operator to eliminate the tuples of a relation which are not contributive to a query. 2-way semijoin [KR87] is an extended version of semijoin which not only performs forward reduction like traditional semijoin does, but also provides backward reduction always in cost-effective way. Bloom Filter[B70] and PERF [LR95] are 2 filter based techniques which use a bit vector to represent of the original join attributes projection during the data transmission. Compare with generating a bit array with hash function in bloom filter, Perf join is based on the tuples scan order to avoid losing information caused by hash collision. In the thesis, we will apply both bloom filter and pert on 2-way semijoin algorithms to reduce transmission cost of distributed queries. Performance of propose algorithms will compare against each others and IFS (Initial Feasible Solution) through amount of experiments. \u27Keywords:\u27 Distributed Query Processing, Semijoin, Bloom Filter, Perf Join

REX: Recursive, Delta-Based Data-Centric Computation

In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and OLAP queries, but most are not robust enough to scale to large clusters. Conversely, "cloud" platforms like MapReduce execute chains of batch tasks across clusters in a fault tolerant way, but have too much overhead to support ad hoc queries. Moreover, both classes of platform incur significant overhead in executing iterative data analysis algorithms. Most such iterative algorithms repeatedly refine portions of their answers, until some convergence criterion is reached. However, general cloud platforms typically must reprocess all data in each step. DBMSs that support recursive SQL are more efficient in that they propagate only the changes in each step -- but they still accumulate each iteration's state, even if it is no longer useful. User-defined functions are also typically harder to write for DBMSs than for cloud platforms. We seek to unify the strengths of both styles of platforms, with a focus on supporting iterative computations in which changes, in the form of deltas, are propagated from iteration to iteration, and state is efficiently updated in an extensible way. We present a programming model oriented around deltas, describe how we execute and optimize such programs in our REX runtime system, and validate that our platform also handles failures gracefully. We experimentally validate our techniques, and show speedups over the competing methods ranging from 2.5 to nearly 100 times.Comment: VLDB201

Optimization of Queries with Conjunction of Predicates

A method to optimize the access at the objects of a relational database is through the optimization of the queries. This article presents an approach of the cost model used in optimization of Select-Project-Join (SPJ) queries with conjunction of predicates and proposes a join optimization algorithm named System RO-H (System Rank Ordering Heuristic). The System RO-H algorithm for optimizing SPJ queries with conjunction of predicates is a System R Dynamic Programming algorithm that extends optimal linear join subplans using a rank-ordering heuristic method as follows: choosing a predicate in ascending order according to the h-metric, where the h-metric depends on the selectivity and the cost per tuple of the predicate, using an expression with heuristic constants.The System Rank-Ordering Heuristic algorithm finds an optimal plan in the space of linear left deep join trees. The System RO-H algorithm saves not a single plan, but multiple optimal plans for every subset, one for each distinct such order, termed interesting order. In order to build an optimal execution plan for a set S of i relations, the optimal plan for each subset of S, consisting of i-1 relations is extended, using the Lemma based on a h-metric for predicates. Optimal plans for subsets are stored and reused. The optimization algorithm chooses a plan of least cost from the execution space