2,952 research outputs found
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
Rumble: Data Independence for Large Messy Data Sets
This paper introduces Rumble, an engine that executes JSONiq queries on
large, heterogeneous and nested collections of JSON objects, leveraging the
parallel capabilities of Spark so as to provide a high degree of data
independence. The design is based on two key insights: (i) how to map JSONiq
expressions to Spark transformations on RDDs and (ii) how to map JSONiq FLWOR
clauses to Spark SQL on DataFrames. We have developed a working implementation
of these mappings showing that JSONiq can efficiently run on Spark to query
billions of objects into, at least, the TB range. The JSONiq code is concise in
comparison to Spark's host languages while seamlessly supporting the nested,
heterogeneous data sets that Spark SQL does not. The ability to process this
kind of input, commonly found, is paramount for data cleaning and curation. The
experimental analysis indicates that there is no excessive performance loss,
occasionally even a gain, over Spark SQL for structured data, and a performance
gain over PySpark. This demonstrates that a language such as JSONiq is a simple
and viable approach to large-scale querying of denormalized, heterogeneous,
arborescent data sets, in the same way as SQL can be leveraged for structured
data sets. The results also illustrate that Codd's concept of data independence
makes as much sense for heterogeneous, nested data sets as it does on highly
structured tables.Comment: Preprint, 9 page
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Personalization by Partial Evaluation.
The central contribution of this paper is to model personalization by the programmatic notion of partial evaluation.Partial evaluation is a technique used to automatically specialize programs, given incomplete information about their input.The methodology presented here models a collection of information resources as a program (which abstracts the underlying schema of organization and flow of information),partially evaluates the program with respect to user input,and recreates a personalized site from the specialized program.This enables a customizable methodology called PIPE that supports the automatic specialization of resources,without enumerating the interaction sequences beforehand .Issues relating to the scalability of PIPE,information integration,sessioniz-ling scenarios,and case studies are presented
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