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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 volume, the 32nd issue of Transactions on
Large-Scale Data- and Knowledge-Centered Systems, focuses on Big
Data Analytics and Knowledge Discovery, and contains extended and
revised versions of five papers selected from the 17th
International Conference on Big Data Analytics and Knowledge
Discovery, DaWaK 2015, held in Valencia, Spain, during September
1-4, 2015. The five papers focus on the exact detection of
information leakage, the binary shapelet transform for multiclass
time series classification, a discrimination-aware association rule
classifier for decision support (DAAR), new word detection and
tagging on Chinese Twitter, and on-demand snapshot maintenance in
data warehouses using incremental ETL pipelines, respectively.
discovery,="" contains="" extended="" revised="" versions=""
five="" papers="" selected="" from="" 17th="" international=""
conference="" discovery="" (dawak="" 2015),="" held="" in=""
valencia,="" spain,="" during="" september="" 1-4,="" 2015.=""
focus="" exact="" detection="" information="" leakage,="" binary=""
shapelet="" transform="" for="" multiclass="" time="" series=""
classification,="" a="" discrimination-aware="" association=""
rule="" classifier="" decision="" support="" (daar),="" new=""
word="" tagging="" chinese="" twitter,="" on-demand="" snapshot=""
maintenance="" warehouses="" using="" incremental="" etl=""
pipelines,="" respectively.
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Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad, India, December 17-20, 2019, Proceedings (Paperback, 1st ed. 2019)
Sanjay Madria, Philippe Fournier-Viger, Sanjay Chaudhary, P. Krishna Reddy
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This book constitutes the refereed proceedings of the 7th
International Conference on Big Data analytics, BDA 2019, held in
Ahmedabad, India, in December 2019.The 25 papers presented in this
volume were carefully reviewed and selected from 53 submissions.
The papers are organized in topical sections named: big data
analytics: vision and perspectives; search and information
extraction; predictive analytics in medical and agricultural
domains; graph analytics; pattern mining; and machine learning.
The sensor cloud is a new model of computing paradigm for Wireless
Sensor Networks (WSNs), which facilitates resource sharing and
provides a platform to integrate different sensor networks where
multiple users can build their own sensing applications at the same
time. It enables a multi-user on-demand sensory system, where
computing, sensing, and wireless network resources are shared among
applications. Therefore, it has inherent challenges for providing
security and privacy across the sensor cloud infrastructure. With
the integration of WSNs with different ownerships, and users
running a variety of applications including their own code, there
is a need for a risk assessment mechanism to estimate the
likelihood and impact of attacks on the life of the network. The
data being generated by the wireless sensors in a sensor cloud need
to be protected against adversaries, which may be outsiders as well
as insiders. Similarly, the code disseminated to the sensors within
the sensor cloud needs to be protected against inside and outside
adversaries. Moreover, since the wireless sensors cannot support
complex and energy-intensive measures, the lightweight schemes for
integrity, security, and privacy of the data have to be redesigned.
The book starts with the motivation and architecture discussion of
a sensor cloud. Due to the integration of multiple WSNs running
user-owned applications and code, the possibility of attacks is
more likely. Thus, next, we discuss a risk assessment mechanism to
estimate the likelihood and impact of attacks on these WSNs in a
sensor cloud using a framework that allows the security
administrator to better understand the threats present and take
necessary actions. Then, we discuss integrity and privacy
preserving data aggregation in a sensor cloud as it becomes harder
to protect data in this environment. Integrity of data can be
compromised as it becomes easier for an attacker to inject false
data in a sensor cloud, and due to hop by hop nature, privacy of
data could be leaked as well. Next, the book discusses a
fine-grained access control scheme which works on the secure
aggregated data in a sensor cloud. This scheme uses Attribute Based
Encryption (ABE) to achieve the objective. Furthermore, to securely
and efficiently disseminate application code in sensor cloud, we
present a secure code dissemination algorithm which first reduces
the amount of code to be transmitted from the base station to the
sensor nodes. It then uses Symmetric Proxy Re-encryption along with
Bloom filters and Hash-based Message Authentication Code (HMACs) to
protect the code against eavesdropping and false code injection
attacks.
This book constitutes the refereed proceedings of the 18th
International Conference on Data Warehousing and Knowledge
Discovery, DaWaK 2016, held in Porto, Portugal, September 2016. The
25 revised full papers presented were carefully reviewed and
selected from 73 submissions. The papers are organized in topical
sections on Mining Big Data, Applications of Big Data Mining, Big
Data Indexing and Searching, Big Data Learning and Security, Graph
Databases and Data Warehousing, Data Intelligence and Technology.
This book constitutes the refereed proceedings of the 17th
International Conference on Data Warehousing and Knowledge
Discovery, DaWaK 2015, held in Valencia, Spain, September 2015. The
31 revised full papers presented were carefully reviewed and
selected from 90 submissions. The papers are organized in topical
sections similarity measure and clustering; data mining; social
computing; heterogeneos networks and data; data warehouses; stream
processing; applications of big data analysis; and big data.
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