Online aggregation is a technique for improving the interactive behavior of database systems processing expensive analytical queries. Almost all database operations are performed in batch mode, i.e. the user issues a query and waits till the database has finished processing the entire query. On the contrary, using online aggregation, the user gets estimates of an aggregate query in an online fashion as soon as the query is issued. For example, if the final answer is 1000, after k seconds, the user gets the estimates in form of a confidence interval like [990, 1020] with 95% probability. This confidence keeps on shrinking as the system gets more and more samples.
Online aggregation was proposed in 1997 by Hellerstein, Haas and Wang [1] for group-by aggregation queries over a single table. Later, the authors showed how to evaluate joins in an online fashion. [2] In 2007, Jermaine et al. designed and implemented a prototype database system called Database-Online (or DBO) that computes group-by aggregate query over multiple tables in an online and more importantly in a scalable fashion. [3] All the approaches for online aggregation use random sampling, which is non-trivial in a distributed environment due to inspection paradox of renewal reward theory. In 2011, Pansare et al. proposed a Bayesian model to deal with the inspection paradox and implemented online aggregation for a MapReduce-like environment. [4]
In computing, online analytical processing, or OLAP, is an approach to quickly answer multi-dimensional analytical (MDA) queries. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.
NonStop SQL is a commercial relational database management system that is designed for fault tolerance and scalability, currently offered by Hewlett Packard Enterprise. The latest version is SQL/MX 3.4.
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. In any given transaction with a variety of items, association rules are meant to discover the rules that determine how or why certain items are connected.
R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. The R-tree was proposed by Antonin Guttman in 1984 and has found significant use in both theoretical and applied contexts. A common real-world usage for an R-tree might be to store spatial objects such as restaurant locations or the polygons that typical maps are made of: streets, buildings, outlines of lakes, coastlines, etc. and then find answers quickly to queries such as "Find all museums within 2 km of my current location", "retrieve all road segments within 2 km of my location" or "find the nearest gas station". The R-tree can also accelerate nearest neighbor search for various distance metrics, including great-circle distance.
Datalog is a declarative logic programming language. While it is syntactically a subset of Prolog, Datalog generally uses a bottom-up rather than top-down evaluation model. This difference yields significantly different behavior and properties from Prolog. It is often used as a query language for deductive databases. Datalog has been applied to problems in data integration, networking, program analysis, and more.
A statistical database is a database used for statistical analysis purposes. It is an OLAP, instead of OLTP system. Modern decision, and classical statistical databases are often closer to the relational model than the multidimensional model commonly used in OLAP systems today.
In computer programming contexts, a data cube is a multi-dimensional ("n-D") array of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data.
MonetDB is an open-source column-oriented relational database management system (RDBMS) originally developed at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. It is designed to provide high performance on complex queries against large databases, such as combining tables with hundreds of columns and millions of rows. MonetDB has been applied in high-performance applications for online analytical processing, data mining, geographic information system (GIS), Resource Description Framework (RDF), text retrieval and sequence alignment processing.
A bitmap index is a special kind of database index that uses bitmaps.
Joseph M. Hellerstein is an American professor of computer science at the University of California, Berkeley, where he works on database systems and computer networks. He co-founded Trifacta with Jeffrey Heer and Sean Kandel in 2012, which stemmed from their research project, Wrangler.
Samuel R. Madden is an American computer scientist specializing in database management systems. He is currently a professor of computer science at the Massachusetts Institute of Technology.
In a SQL database query, a correlated subquery is a subquery that uses values from the outer query. Because the subquery may be evaluated once for each row processed by the outer query, it can be slow.
Michael Ralph Stonebraker is a computer scientist specializing in database systems. Through a series of academic prototypes and commercial startups, Stonebraker's research and products are central to many relational databases. He is also the founder of many database companies, including Ingres Corporation, Illustra, Paradigm4, StreamBase Systems, Tamr, Vertica and VoltDB, and served as chief technical officer of Informix. For his contributions to database research, Stonebraker received the 2014 Turing Award, often described as "the Nobel Prize for computing."
Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is very useful. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image or the popularity of an image, and to discover manipulated versions and derivative works.
QUEL is a relational database query language, based on tuple relational calculus, with some similarities to SQL. It was created as a part of the Ingres DBMS effort at University of California, Berkeley, based on Codd's earlier suggested but not implemented Data Sub-Language ALPHA. QUEL was used for a short time in most products based on the freely available Ingres source code, most notably in an implementation called POSTQUEL supported by POSTGRES. As Oracle and DB2 gained market share in the early 1980s, most companies then supporting QUEL moved to SQL instead. QUEL continues to be available as a part of the Ingres DBMS, although no QUEL-specific language enhancements have been added for many years.
The term is used for two different things:
Laura M. Haas is an American computer scientist noted for her research in database systems and information integration. She is best known for creating systems and tools for the integration of heterogeneous data from diverse sources, including federated technology that virtualizes access to data, and mapping technology that enables non-programmers to specify how data should be integrated.
Discovering communities in a network, known as community detection/discovery, is a fundamental problem in network science, which attracted much attention in the past several decades. In recent years, with the tremendous studies on big data, another related but different problem, called community search, which aims to find the most likely community that contains the query node, has attracted great attention from both academic and industry areas. It is a query-dependent variant of the community detection problem. A detailed survey of community search can be found at ref., which reviews all the recent studies
Gautam Das is a computer scientist in the field of databases research. He is an ACM Fellow and IEEE Fellow.
Apache Pinot is a column-oriented, open-source, distributed data store written in Java. Pinot is designed to execute OLAP queries with low latency. It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion. The name Pinot comes from the Pinot grape vines that are pressed into liquid that is used to produce a variety of different wines. The founders of the database chose the name as a metaphor for analyzing vast quantities of data from a variety of different file formats or streaming data sources.