Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

Differential dependencies: Reasoning and discovery

Abstract: The importance of difference semantics (e.g., “similar” or “dissimilar”) has been recently recognized for declaring dependencies among various types of data, such as numerical values or text values. We propose a novel form of Differential Dependencies (dds), which specifies constraints on difference, called differential functions, instead of identification functions in traditional dependency notations like functional dependencies. Informally, a differential dependency states that if two tuples have distances on attributes X agreeing with a certain differential function, then their distances on attributes Y should also agree with the corresponding differential function on Y. For example, [date(l 7)]→[price(

Reverse Query Processing

Abstract: Generating databases for testing database applications (e.g., OLAP or business objects) is a daunting task in practice. There are a number of commercial tools to automatically generate test databases. These tools take a database schema (table layouts plus integrity constraints) and table sizes as input in order to generate new tuples. However, the databases generated by these tools are not adequate for testing a database application. If an application query is executed against such a synthetic database, then the result of that application query is likely to be empty or contain weird results, such as a report on the performance of a sales person that contains negative sales. To solve this problem, this paper proposes a new technique called reverse query processing (RQP). RQP gets a query and a result as input and returns a possible database instance that could have produced that result for that query. RQP also has other applications; most notably, testing the performance of DBMS and debugging SQL queries.

Causality in Databases

Abstract: Provenance is often used to validate data, by verifying its origin and explaining its derivation. When searching for “causes” of tuples in the query results or in general observations, the analysis of lineage becomes an essential tool for providing such justifications. However, lineage can quickly grow very large, limiting its immediate use for providing intuitive explanations to the user. The formal notion of causality is a more refined concept that identifies causes for observations based on user-defined criteria, and that assigns to them gradual degrees of responsibility based on their respective contributions. In this paper, we initiate a discussion on causality in databases, give some simple definitions, and motivate this formalism through a number of example applications.

Database system and method employing data cube operator for group-by operations

Abstract: Disclosed is a system and method for performing database queries including GROUP-BY operations, in which aggregate values for attributes are desired for distinct, partitioned subsets of tuples satisfying a query. A special case of the aggregation problem is addressed, employing a structure, called the data cube operator, which provides information useful for expediting execution of GROUP-BY operations in queries. Algorithms are provided for constructing the data cube by efficiently computing a collection of GROUP-BYs on the attributes of the relation. Decision support systems often require computation of multiple GROUP-BY operations on a given set of attributes, the GROUP-BYs being related in the sense that their attributes are subsets or supersets of each other. The invention extends hash-based and sort-based grouping methods with optimizations, including combining common operations across multiple GROUP-BYs and using pre-computed GROUP-BYs for computing other GROUP-BYs. An extension of the cube algorithms handles any given collection of aggregates.