10,457 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
Towards a Scalable Dynamic Spatial Database System
With the rise of GPS-enabled smartphones and other similar mobile devices,
massive amounts of location data are available. However, no scalable solutions
for soft real-time spatial queries on large sets of moving objects have yet
emerged. In this paper we explore and measure the limits of actual algorithms
and implementations regarding different application scenarios. And finally we
propose a novel distributed architecture to solve the scalability issues.Comment: (2012
Efficient Management of Short-Lived Data
Motivated by the increasing prominence of loosely-coupled systems, such as
mobile and sensor networks, which are characterised by intermittent
connectivity and volatile data, we study the tagging of data with so-called
expiration times. More specifically, when data are inserted into a database,
they may be tagged with time values indicating when they expire, i.e., when
they are regarded as stale or invalid and thus are no longer considered part of
the database. In a number of applications, expiration times are known and can
be assigned at insertion time. We present data structures and algorithms for
online management of data tagged with expiration times. The algorithms are
based on fully functional, persistent treaps, which are a combination of binary
search trees with respect to a primary attribute and heaps with respect to a
secondary attribute. The primary attribute implements primary keys, and the
secondary attribute stores expiration times in a minimum heap, thus keeping a
priority queue of tuples to expire. A detailed and comprehensive experimental
study demonstrates the well-behavedness and scalability of the approach as well
as its efficiency with respect to a number of competitors.Comment: switched to TimeCenter latex styl
AsterixDB: A Scalable, Open Source BDMS
AsterixDB is a new, full-function BDMS (Big Data Management System) with a
feature set that distinguishes it from other platforms in today's open source
Big Data ecosystem. Its features make it well-suited to applications like web
data warehousing, social data storage and analysis, and other use cases related
to Big Data. AsterixDB has a flexible NoSQL style data model; a query language
that supports a wide range of queries; a scalable runtime; partitioned,
LSM-based data storage and indexing (including B+-tree, R-tree, and text
indexes); support for external as well as natively stored data; a rich set of
built-in types; support for fuzzy, spatial, and temporal types and queries; a
built-in notion of data feeds for ingestion of data; and transaction support
akin to that of a NoSQL store.
Development of AsterixDB began in 2009 and led to a mid-2013 initial open
source release. This paper is the first complete description of the resulting
open source AsterixDB system. Covered herein are the system's data model, its
query language, and its software architecture. Also included are a summary of
the current status of the project and a first glimpse into how AsterixDB
performs when compared to alternative technologies, including a parallel
relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data
analytics platform, for things that both technologies can do. Also included is
a brief description of some initial trials that the system has undergone and
the lessons learned (and plans laid) based on those early "customer"
engagements
Designing a resource-efficient data structure for mobile data systems
Designing data structures for use in mobile devices requires attention on optimising data volumes with associated benefits for data transmission, storage space and battery use. For semi-structured data, tree summarisation techniques can be used to reduce the volume of structured elements while dictionary compression can efficiently deal with value-based predicates. This project seeks to investigate and evaluate an integration of the two approaches. The key strength of this technique is that both structural and value predicates could be resolved within one graph while further allowing for compression of the resulting data structure. As the current trend is towards the requirement for working with larger semi-structured data sets this work would allow for the utilisation of much larger data sets whilst reducing requirements on bandwidth and minimising the memory necessary both for the storage and querying of the data
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
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