38,869 research outputs found
Query-Driven Sampling for Collective Entity Resolution
Probabilistic databases play a preeminent role in the processing and
management of uncertain data. Recently, many database research efforts have
integrated probabilistic models into databases to support tasks such as
information extraction and labeling. Many of these efforts are based on batch
oriented inference which inhibits a realtime workflow. One important task is
entity resolution (ER). ER is the process of determining records (mentions) in
a database that correspond to the same real-world entity. Traditional pairwise
ER methods can lead to inconsistencies and low accuracy due to localized
decisions. Leading ER systems solve this problem by collectively resolving all
records using a probabilistic graphical model and Markov chain Monte Carlo
(MCMC) inference. However, for large datasets this is an extremely expensive
process. One key observation is that, such exhaustive ER process incurs a huge
up-front cost, which is wasteful in practice because most users are interested
in only a small subset of entities. In this paper, we advocate pay-as-you-go
entity resolution by developing a number of query-driven collective ER
techniques. We introduce two classes of SQL queries that involve ER operators
--- selection-driven ER and join-driven ER. We implement novel variations of
the MCMC Metropolis Hastings algorithm to generate biased samples and
selectivity-based scheduling algorithms to support the two classes of ER
queries. Finally, we show that query-driven ER algorithms can converge and
return results within minutes over a database populated with the extraction
from a newswire dataset containing 71 million mentions
A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters
Research interest in Grid computing has grown significantly over the past
five years. Management of distributed resources is one of the key issues in
Grid computing. Central to management of resources is the effectiveness of
resource allocation as it determines the overall utility of the system. The
current approaches to superscheduling in a grid environment are non-coordinated
since application level schedulers or brokers make scheduling decisions
independently of the others in the system. Clearly, this can exacerbate the
load sharing and utilization problems of distributed resources due to
suboptimal schedules that are likely to occur. To overcome these limitations,
we propose a mechanism for coordinated sharing of distributed clusters based on
computational economy. The resulting environment, called
\emph{Grid-Federation}, allows the transparent use of resources from the
federation when local resources are insufficient to meet its users'
requirements. The use of computational economy methodology in coordinating
resource allocation not only facilitates the QoS based scheduling, but also
enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE
Cluster'05, Boston, M
Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources
The growing deployment of sensors as part of Internet of Things (IoT) is
generating thousands of event streams. Complex Event Processing (CEP) queries
offer a useful paradigm for rapid decision-making over such data sources. While
often centralized in the Cloud, the deployment of capable edge devices on the
field motivates the need for cooperative event analytics that span Edge and
Cloud computing. Here, we identify a novel problem of query placement on edge
and Cloud resources for dynamically arriving and departing analytic dataflows.
We define this as an optimization problem to minimize the total makespan for
all event analytics, while meeting energy and compute constraints of the
resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for
such dynamic dataflows, and validate them using detailed simulations for 100 -
1000 edge devices and VMs. The results show that our heuristics offer
O(seconds) planning time, give a valid and high quality solution in all cases,
and reduce the number of query migrations. Furthermore, rebalance strategies
when applied in these heuristics have significantly reduced the makespan by
around 20 - 25%.Comment: 11 pages, 7 figure
Efficient Multi-way Theta-Join Processing Using MapReduce
Multi-way Theta-join queries are powerful in describing complex relations and
therefore widely employed in real practices. However, existing solutions from
traditional distributed and parallel databases for multi-way Theta-join queries
cannot be easily extended to fit a shared-nothing distributed computing
paradigm, which is proven to be able to support OLAP applications over immense
data volumes. In this work, we study the problem of efficient processing of
multi-way Theta-join queries using MapReduce from a cost-effective perspective.
Although there have been some works using the (key,value) pair-based
programming model to support join operations, efficient processing of multi-way
Theta-join queries has never been fully explored. The substantial challenge
lies in, given a number of processing units (that can run Map or Reduce tasks),
mapping a multi-way Theta-join query to a number of MapReduce jobs and having
them executed in a well scheduled sequence, such that the total processing time
span is minimized. Our solution mainly includes two parts: 1) cost metrics for
both single MapReduce job and a number of MapReduce jobs executed in a certain
order; 2) the efficient execution of a chain-typed Theta-join with only one
MapReduce job. Comparing with the query evaluation strategy proposed in [23]
and the widely adopted Pig Latin and Hive SQL solutions, our method achieves
significant improvement of the join processing efficiency.Comment: VLDB201
H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution
The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we addressthe challenges of automatically adapting the distribution of data in a cluster to the workload imposed by the input applications. We propose a generic algorithm, named H-WorD, which, based on the estimated workload over resources, suggests alternative execution scenarios of tasks, and hence identifies required transfers of input data a priori, for timely bringing data close to the execution. We exemplify our algorithm in the context of MapReduce jobs in a Hadoop ecosystem. Finally, we evaluate our approach and demonstrate the performance gains of automatic data redistribution.Peer ReviewedPostprint (author's final draft
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