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The LNCS journal Transactions on Large-Scale Data- and
Knowledge-Centered Systems focuses on data management, knowledge
discovery, and knowledge processing, which are core and hot topics
in computer science. Since the 1990s, the Internet has become the
main driving force behind application development in all domains.
An increase in the demand for resource sharing across different
sites connected through networks has led to an evolution of data-
and knowledge-management systems from centralized systems to
decentralized systems enabling large-scale distributed applications
providing high scalability. Current decentralized systems still
focus on data and knowledge as their main resource. Feasibility of
these systems relies basically on P2P (peer-to-peer) techniques and
the support of agent systems with scaling and decentralized
control. Synergy between grids, P2P systems, and agent technologies
is the key to data- and knowledge-centered systems in large-scale
environments. This special issue contains extended and revised
versions of 4 papers, selected from the 25 papers presented at the
satellite events associated with the 17th East-European Conference
on Advances in Databases and Information Systems (ADBIS 2013), held
on September 1-4, 2013 in Genoa, Italy. The three satellite events
were GID 2013, the Second International Workshop on GPUs in
Databases; SoBI 2013, the First International Workshop on Social
Business Intelligence: Integrating Social Content in Decision
Making; and OAIS 2013, the Second International Workshop on
Ontologies Meet Advanced Information Systems. The papers cover
various topics in large-scale data and knowledge-centered systems,
including GPU-accelerated database systems and GPU-based
compression for large time series databases, design of parallel
data warehouses, and schema matching. The special issue content,
which combines both theoretical and application-based
contributions, gives a useful overview of some of the current
trends in large-scale data and knowledge management and will
stimulate new ideas for further research and development within
both the scientific and industrial communities.
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New Trends in Database and Information Systems II - Selected papers of the 18th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events, ADBIS 2014 Ohrid, Macedonia, September 7-10, 2014 Proceedings II (Paperback, 2015 ed.)
Nick Bassiliades, Mirjana Ivanovic, Margita Kon-Popovska, Yannis Manolopoulos, Themis Palpanas, …
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Discovery Miles 55 560
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This volume contains the papers of 3 workshops and the doctoral
consortium, which are organized in the framework of the 18th
East-European Conference on Advances in Databases and Information
Systems (ADBIS'2014). The 3rd International Workshop on GPUs in
Databases (GID'2014) is devoted to subjects related to utilization
of Graphics Processing Units in database environments. The use of
GPUs in databases has not yet received enough attention from the
database community. The intention of the GID workshop is to provide
a discussion on popularizing the GPUs and providing a forum for
discussion with respect to the GID's research ideas and their
potential to achieve high speedups in many database applications.
The 3rd International Workshop on Ontologies Meet Advanced
Information Systems (OAIS'2014) has a twofold objective to present:
new and challenging issues in the contribution of ontologies for
designing high quality information systems, and new research and
technological developments which use ontologies all over the life
cycle of information systems. The 1st International Workshop on
Technologies for Quality Management in Challenging Applications
(TQMCA'2014) focuses on quality management and its importance in
new fields such as big data, crowd-sourcing, and stream databases.
The Workshop has addressed the need to develop novel approaches and
technologies, and to entirely integrate quality management into
information system management.
This book reports on state-of-art research and applications in
the field of databases and information systems. It includes both
fourteen selected short contributions, presented at the
East-European Conference on Advances in Databases and Information
Systems (ADBIS 2013, September 1-4, Genova, Italy), and twenty-six
papers from ADBIS 2013 satellite events. The short contributions
from the main conference are collected in the first part of the
book, which covers a wide range of topics, like data management,
similarity searches, spatio-temporal and social network data, data
mining, data warehousing, and data management on novel
architectures, such as graphics processing units, parallel database
management systems, cloud and MapReduce environments. In contrast,
the contributions from the satellite events are organized in five
different parts, according to their respective ADBIS satellite
event: BiDaTA 2013 - Special Session on Big Data: New Trends and
Applications); GID 2013 The Second International Workshop on GPUs
in Databases; OAIS 2013 The Second International Workshop on
Ontologies Meet Advanced Information Systems; SoBI 2013 The First
International Workshop on Social Business Intelligence: Integrating
Social Content in Decision Making; and last but not least, the
Doctoral Consortium, a forum for Ph.D. students. The book, which
addresses academics and professionals alike, provides the readers
with a comprehensive and timely overview of new trends in database
and information systems research, and promotes new ideas and
collaborations among the different research communities of the
eastern European countries and the rest of the world.
"
Entity Resolution (ER) lies at the core of data integration and
cleaning and, thus, a bulk of the research examines ways for
improving its effectiveness and time efficiency. The initial ER
methods primarily target Veracity in the context of structured
(relational) data that are described by a schema of well-known
quality and meaning. To achieve high effectiveness, they leverage
schema, expert, and/or external knowledge. Part of these methods
are extended to address Volume, processing large datasets through
multi-core or massive parallelization approaches, such as the
MapReduce paradigm. However, these early schema-based approaches
are inapplicable to Web Data, which abound in voluminous, noisy,
semi-structured, and highly heterogeneous information. To address
the additional challenge of Variety, recent works on ER adopt a
novel, loosely schema-aware functionality that emphasizes
scalability and robustness to noise. Another line of present
research focuses on the additional challenge of Velocity, aiming to
process data collections of a continuously increasing volume. The
latest works, though, take advantage of the significant
breakthroughs in Deep Learning and Crowdsourcing, incorporating
external knowledge to enhance the existing words to a significant
extent. This synthesis lecture organizes ER methods into four
generations based on the challenges posed by these four Vs. For
each generation, we outline the corresponding ER workflow, discuss
the state-of-the-art methods per workflow step, and present current
research directions. The discussion of these methods takes into
account a historical perspective, explaining the evolution of the
methods over time along with their similarities and differences.
The lecture also discusses the available ER tools and benchmark
datasets that allow expert as well as novice users to make use of
the available solutions.
Data usually comes in a plethora of formats and dimensions,
rendering the exploration and information extraction processes
challenging. Thus, being able to perform exploratory analyses in
the data with the intent of having an immediate glimpse on some of
the data properties is becoming crucial. Exploratory analyses
should be simple enough to avoid complicate declarative languages
(such as SQL) and mechanisms, and at the same time retain the
flexibility and expressiveness of such languages. Recently, we have
witnessed a rediscovery of the so-called example-based methods, in
which the user, or the analyst, circumvents query languages by
using examples as input. An example is a representative of the
intended results, or in other words, an item from the result set.
Example-based methods exploit inherent characteristics of the data
to infer the results that the user has in mind, but may not able to
(easily) express. They can be useful in cases where a user is
looking for information in an unfamiliar dataset, when the task is
particularly challenging like finding duplicate items, or simply
when they are exploring the data. In this book, we present an
excursus over the main methods for exploratory analysis, with a
particular focus on example-based methods. We show how that
different data types require different techniques, and present
algorithms that are specifically designed for relational, textual,
and graph data. The book presents also the challenges and the new
frontiers of machine learning in online settings which recently
attracted the attention of the database community. The lecture
concludes with a vision for further research and applications in
this area.
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