18,606 research outputs found
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
LogBase: A Scalable Log-structured Database System in the Cloud
Numerous applications such as financial transactions (e.g., stock trading)
are write-heavy in nature. The shift from reads to writes in web applications
has also been accelerating in recent years. Write-ahead-logging is a common
approach for providing recovery capability while improving performance in most
storage systems. However, the separation of log and application data incurs
write overheads observed in write-heavy environments and hence adversely
affects the write throughput and recovery time in the system. In this paper, we
introduce LogBase - a scalable log-structured database system that adopts
log-only storage for removing the write bottleneck and supporting fast system
recovery. LogBase is designed to be dynamically deployed on commodity clusters
to take advantage of elastic scaling property of cloud environments. LogBase
provides in-memory multiversion indexes for supporting efficient access to data
maintained in the log. LogBase also supports transactions that bundle read and
write operations spanning across multiple records. We implemented the proposed
system and compared it with HBase and a disk-based log-structured
record-oriented system modeled after RAMCloud. The experimental results show
that LogBase is able to provide sustained write throughput, efficient data
access out of the cache, and effective system recovery.Comment: VLDB201
A Backend Framework for the Efficient Management of Power System Measurements
Increased adoption and deployment of phasor measurement units (PMU) has
provided valuable fine-grained data over the grid. Analysis over these data can
provide insight into the health of the grid, thereby improving control over
operations. Realizing this data-driven control, however, requires validating,
processing and storing massive amounts of PMU data. This paper describes a PMU
data management system that supports input from multiple PMU data streams,
features an event-detection algorithm, and provides an efficient method for
retrieving archival data. The event-detection algorithm rapidly correlates
multiple PMU data streams, providing details on events occurring within the
power system. The event-detection algorithm feeds into a visualization
component, allowing operators to recognize events as they occur. The indexing
and data retrieval mechanism facilitates fast access to archived PMU data.
Using this method, we achieved over 30x speedup for queries with high
selectivity. With the development of these two components, we have developed a
system that allows efficient analysis of multiple time-aligned PMU data
streams.Comment: Published in Electric Power Systems Research (2016), not available
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