Derivation of incremental equations for PNF nested relations

Incremental view maintenance techniques are required for many new types of data models that are being increasingly used in industry. One of these models is the nested relational model that is used in the modelling complex objects in databases. In this paper we derive a group of expressions for incrementally evaluating query expressions in the nested relational model. We also present an algorithm to propagate base relation updates to a materialized view when the view is defined as a complex query

Compiling Away Set Containment and Intersection Joins

We investigate the effect of query rewriting on joins involving set-valued attributes in object-relational database management systems. We show that by unnesting set-valued attributes (that are stored in an internal nested representation) prior to the actual set containment or intersection join we can improve the performance of query evaluation by an order of magnitude. By giving example query evaluation plans we show the increased possibilities for the query optimizer

Algorithm Choice For Multiple-Query Evaluation

Traditional query optimization concentrates on the optimization of the execution of each individual query. More recently, it has been observed that by considering a sequence of multiple queries some additional high-level optimizations can be performed. Once these optimizations have been performed, each operation is translated into executable code. The fundamental insight in this paper is that significant improvements can be gained by careful choice of the algorithm to be used for each operation. This choice is not merely based on efficiency of algorithms for individual operations, but rather on the efficiency of the algorithm choices for the entire multiple-query evaluation. An efficient procedure for automatically optimizing these algorithm choices is given

Automated synthesis of data extraction and transformation programs

Due to the abundance of data in today’s data-rich world, end-users increasingly need to perform various data extraction and transformation tasks. While many of these tedious tasks can be performed in a programmatic way, most end-users lack the required programming expertise to automate them and end up spending their valuable time in manually performing various data- related tasks. The field of program synthesis aims to overcome this problem by automatically generating programs from informal specifications, such as input-output examples or natural language. This dissertation focuses on the design and implementation of new systems for automating important classes of data transformation and extraction tasks. It introduces solutions for automating data manipulation tasks on fully- structured data formats like relational tables, or on semi-structured formats such as XML and JSON documents. First, we describe a novel algorithm for synthesizing hierarchical data transformations from input-output examples. A key novelty of our approach is that it reduces the synthesis of tree transformations to the simpler problem of synthesizing transformations over the paths of the tree. We also describe a new and effective algorithm for learning path transformations that combines logical SMT-based reasoning with machine learning techniques based on decision trees. Next, we present a new methodology for learning programs that migrate tree-structured documents to relational table representations from input-output examples. Our approach achieves its goal by decomposing the synthesis task to two subproblems of (A) learning the column extraction logic, and (B) learning the row extraction logic. We propose a technique for learning column extraction programs using deterministic finite automata, and a new algorithm for predicate learning which combines integer linear programing and logic minimization. Finally, we address the problem of automating data extraction tasks from natural language. Specifically, we focus on data retrieval from relational databases and describe a novel approach for learning SQL queries from English descriptions. The method we describe is fully automatic and database-agnostic (i.e., does not require customization for each database). Our method combines semantic parsing techniques from the NLP community with novel programming languages ideas involving probabilistic type inhabitation and automated sketch repair.Computer Science

Performance Evaluation of Outer Join Operations on Adds System

This paper describes the performance evaluation of an outerjoin operation on the ADDS system. It includes the definition of outerjoin, the algorithms used, the test results, and the recommendation of the evaluation.Computing and Information Science