11,233 research outputs found
Towards a Holistic Integration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management
Spreadsheet software is the tool of choice for interactive ad-hoc data
management, with adoption by billions of users. However, spreadsheets are not
scalable, unlike database systems. On the other hand, database systems, while
highly scalable, do not support interactivity as a first-class primitive. We
are developing DataSpread, to holistically integrate spreadsheets as a
front-end interface with databases as a back-end datastore, providing
scalability to spreadsheets, and interactivity to databases, an integration we
term presentational data management (PDM). In this paper, we make a first step
towards this vision: developing a storage engine for PDM, studying how to
flexibly represent spreadsheet data within a database and how to support and
maintain access by position. We first conduct an extensive survey of
spreadsheet use to motivate our functional requirements for a storage engine
for PDM. We develop a natural set of mechanisms for flexibly representing
spreadsheet data and demonstrate that identifying the optimal representation is
NP-Hard; however, we develop an efficient approach to identify the optimal
representation from an important and intuitive subclass of representations. We
extend our mechanisms with positional access mechanisms that don't suffer from
cascading update issues, leading to constant time access and modification
performance. We evaluate these representations on a workload of typical
spreadsheets and spreadsheet operations, providing up to 20% reduction in
storage, and up to 50% reduction in formula evaluation time
Optimization in Knowledge-Intensive Crowdsourcing
We present SmartCrowd, a framework for optimizing collaborative
knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by
accounting for human factors in the process of assigning tasks to workers.
Human factors designate workers' expertise in different skills, their expected
minimum wage, and their availability. In SmartCrowd, we formulate task
assignment as an optimization problem, and rely on pre-indexing workers and
maintaining the indexes adaptively, in such a way that the task assignment
process gets optimized both qualitatively, and computation time-wise. We
present rigorous theoretical analyses of the optimization problem and propose
optimal and approximation algorithms. We finally perform extensive performance
and quality experiments using real and synthetic data to demonstrate that
adaptive indexing in SmartCrowd is necessary to achieve efficient high quality
task assignment.Comment: 12 page
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
From social networks to language modeling, the growing scale and importance
of graph data has driven the development of numerous new graph-parallel systems
(e.g., Pregel, GraphLab). By restricting the computation that can be expressed
and introducing new techniques to partition and distribute the graph, these
systems can efficiently execute iterative graph algorithms orders of magnitude
faster than more general data-parallel systems. However, the same restrictions
that enable the performance gains also make it difficult to express many of the
important stages in a typical graph-analytics pipeline: constructing the graph,
modifying its structure, or expressing computation that spans multiple graphs.
As a consequence, existing graph analytics pipelines compose graph-parallel and
data-parallel systems using external storage systems, leading to extensive data
movement and complicated programming model.
To address these challenges we introduce GraphX, a distributed graph
computation framework that unifies graph-parallel and data-parallel
computation. GraphX provides a small, core set of graph-parallel operators
expressive enough to implement the Pregel and PowerGraph abstractions, yet
simple enough to be cast in relational algebra. GraphX uses a collection of
query optimization techniques such as automatic join rewrites to efficiently
implement these graph-parallel operators. We evaluate GraphX on real-world
graphs and workloads and demonstrate that GraphX achieves comparable
performance as specialized graph computation systems, while outperforming them
in end-to-end graph pipelines. Moreover, GraphX achieves a balance between
expressiveness, performance, and ease of use
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
Design and Implementation of the UniProt Website
The UniProt consortium is the main provider of protein sequence and annotation data for much of the life sciences community. The "www.uniprot.org":http://www.uniprot.org website is the primary access point to this data and to documentation and basic tools for the data. This paper discusses the design and implementation of the new website, which was released in July 2008, and shows how it improves data access for users with different levels of experience, as well as to machines for programmatic access
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