17356153
CORDS: automatic discovery of correlations and soft functional dependencies
The rich dependency structure found in the columns of real-world relational databases can be exploited to great advantage, but can also cause query optimizers---which usually assume that columns are statistically independent---to underestimate the selectivities of conjunctive predicates by orders of magnitude. We introduce CORDS, an efficient and scalable tool for automatic discovery of correlations and soft functional dependencies between columns. CORDS searches for column pairs that might have interesting and useful dependency relations by systematically enumerating candidate pairs and simultaneously pruning unpromising candidates using a flexible set of heuristics. A robust chi-squared analysis is applied to a sample of column values in order to identify correlations, and the number of distinct values in the sampled columns is analyzed to detect soft functional dependencies. CORDS can be used as a data mining tool, producing dependency graphs that are of intrinsic interest. We focus primarily on the use of CORDS in query optimization. Specifically, CORDS recommends groups of columns on which to maintain certain simple joint statistics. These "column-group" statistics are then used by the optimizer to avoid naive selectivity estimates based on inappropriate independence assumptions. This approach, because of its simplicity and judicious use of sampling, is relatively easy to implement in existing commercial systems, has very low overhead, and scales well to the large numbers of columns and large table sizes found in real-world databases. Experiments with a prototype implementation show that the use of CORDS in query optimization can speed up query execution times by an order of magnitude. CORDS can be used in tandem with query feedback systems such as the LEO learning optimizer, leveraging the infrastructure of such systems to correct bad selectivity estimates and ameliorating the poor performance of feedback systems during slow learning phases.
16124888
GORDIAN: efficient and scalable discovery of composite keys
Identification of (composite) key attributes is of fundamental importance for many different data management tasks such as data modeling, data integration, anomaly detection, query formulation, query optimization, and indexing. However, information about keys is often missing or incomplete in many real-world database scenarios. Surprisingly, the fundamental problem of automatic key discovery has received little attention in the existing literature. Existing solutions ignore composite keys, due to the complexity associated with their discovery. Even for simple keys, current algorithms take a brute-force approach; the resulting exponential CPU and memory requirements limit the applicability of these methods to small datasets. In this paper, we describe GORDIAN, a scalable algorithm for automatic discovery of keys in large datasets, including composite keys. GORDIAN can provide exact results very efficiently for both real-world and synthetic datasets. GORDIAN can be used to find (composite) key attributes in any collection of entities, e.g., key column-groups in relational data, or key leaf-node sets in a collection of XML documents with a common schema. We show empirically that GORDIAN can be combined with sampling to efficiently obtain high quality sets of approximate keys even in very large datasets.
739681
BHUNT: Automatic Discovery of Fuzzy Algebraic Constraints in Relational Data
We present the BHUNT scheme for automatically discovering algebraic constraints between pairs of columns in relational data. The constraints may be "fuzzy" in that they hold for most, but not all, of the records, and the columns may be in the same table or different tables. Such constraints are of interest in the context of both data mining and query optimization, and the BHUNT methodology can potentially be adapted to discover fuzzy functional dependencies and other useful relationships. BHUNT first identifies candidate sets of column value pairs that are likely to satisfy an algebraic constraint. This discovery process exploits both system catalog information and data samples, and employs pruning heuristics to control processing costs. For each candidate, BHUNT constructs algebraic constraints by applying statistical histogramming, segmentation, or clustering techniques to samples of column values. Using results from the theory of tolerance intervals, the sample sizes can be chosen to control the number of "exception" records that fail to satisfy the discovered constraints. In query-optimization mode, BHUNT can automatically partition the data into normal and exception records. During subsequent query processing, queries can be modified to incorporate the constraints; the optimizer uses the constraints to identify new, more efficient access paths. The results are then combined with the results of executing the original query against the (small) set of exception records. Experiments on a very large database using a prototype implementation of BHUNT show reductions in table accesses of up to two orders of magnitude, leading to speedups in query processing by up to a factor of 6.8.
17228561
DAMIA - A Data Mashup Fabric for Intranet Applications
Damia is a lightweight enterprise data integration service where line of business users can create and catalog high value data feeds for consumption by situational applications. Damia is inspired by the Web 2.0 mashup phenomenon. It consists of (1) a browser-based user-interface that allows for the specification of data mashups as data flowfgraphs using a set of operators, (2) a server with an execution engine, as well as (3) APIs for searching, debugging, executing and managing mashups. Damia offers a framework and functionality for dynamic entity resolution, streaming and other higher value features particularly important in the enterprise domain. Damia is currently in perpetual beta in the IBM Intranet. 
 
In this demonstration, we showcase the creation and execution of several enterprise data mashups, thereby illustrating the architecture and features of the overall Damia system.
5349915
Distributed Component Database Management Systems
Publisher Summary A component database management system (DBMS) is a system software framework that application developers can extend by embedding modules of programming logic in it. These extensions implement database objects such as new data types, application-level object classes, analytic algorithms, and external data-access facilities. This chapter describes how distributed object relational database management systems (ORDBMSs) are constructed and how they can be used. Distributed component DBMSs such as ORDBMSs are useful because they address requirements that are not served well by traditional distributed systems technologies. Distributed ORDBMSs constitute a powerful, innovative model for generalized distributed computing. It is a unique technology that provides a framework for managing both logic and data in an abstract data model. This review is useful because the commercial versions of distributed ORDBMSs take such a system as a starting point and because detailed descriptions of ORDBMS internals are not widely available. Instances of these single-site ORDBMSs run on each node of the overall distributed system. Furthermore, this chapter outlines the architecture of a distributed ORDBMS and describes the modifications that are necessary for a single-site ORDBMS to operate in a distributed environment.
2984369
BigSur: A System For the Management of Earth Science Data
In this paper we present a prototype system for the management of earth science data which is novel in that it takes a DBMS centric view of the the task. Our prototype -called "BigSur" -is shown in the context of its use by two geographically distributed scientific groups with demanding data storage and processing requirements. BigSur currently stores 1 Terabyte of data, about one thousandth of the volume EOSDIS must store. We claim that the design principles embodied in BigSur provide sufficient flexibility to achieve the difficult scientific and technical objectives of Mission to Planet Earth.
8309392
The ethics and aesthetics of the image interface
Now that we have developed and are introducing media-rich computer human interfaces, it is important to develop a taxonomy that can help us appreciate the power and value of computer systems in order to better quantify them and assess their creative and ethical impact. To date the application of semiosis has been to natural languages and human-human communications. The author suggests that this can now be applied to artificial languages and in particular, their use in human computer interactions, imaging and visualization. The semiology of Charles Sanders Peirce can be applied to this field and may be used to assess the aesthetic (creative) and ethical implications of computer human interface (CHI), imaging and visualization technology and the way it is being developed and used.
5310188
My gasket
30490732
Reality versus imagination