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Divyakant Agrawal Data Management in the Cloud

Kenett Ron S. Operational Risk Management. A Practical Approach to Intelligent Data Analysis


Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management. Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines. Explores integration of semantic, unstructured textual data, in Operational Risk Management. Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies. Presents a comprehensive treatment of «near-misses» data and incidents in Operational Risk Management. Looks at case studies in the financial and industrial sector. Discusses application of ontology engineering to model knowledge used in Operational Risk Management. Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.

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Mark Allen Master Data Management in Practice. Achieving True Customer MDM


In this book, authors Dalton Cervo and Mark Allen show you how to implement Master Data Management (MDM) within your business model to create a more quality controlled approach. Focusing on techniques that can improve data quality management, lower data maintenance costs, reduce corporate and compliance risks, and drive increased efficiency in customer data management practices, the book will guide you in successfully managing and maintaining your customer master data. You'll find the expert guidance you need, complete with tables, graphs, and charts, in planning, implementing, and managing MDM.

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Tim King Managing Data Quality


Data is an increasingly important business asset and enabler for organisational activities. Data quality is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability.
This book explains data quality management in practical terms, focusing on three key areas – the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.

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Skip Farmer Digital Data Integrity


How to plan your future strategy for efficient, cost-saving data management Businesses have historically treated data protection as an afterthought, as simply making an occasional copy of data that could be used in the future. Today, this attitude is changing rapidly. The ever-increasing amount of data, along with the emphasis on continuous availability, necessitates changes in the approach to data integrity, which results in management and protection becoming much more closely aligned. Digital Data Integrity throws light on the data integrity landscape of the future. It provides the reader with a brief overview of the historical methods and subsequent evolution of data protection. The text shows how the whole subject of data integrity is changing and describes and positions many of the new, enhanced, more intelligent protection technologies and methods. Digital Data Integrity: Takes a unique, forward look at data protection and management, highlighting the paradigm shift from simple backup and recovery to total data management. Details recent developments in compliance regulations in an accessible manner. Covers enhanced protection technologies such as advanced intelligent synthetic backups, data reduction methods, and data growth – online protection using continuous data protection. Explains data life cycle management and data storage, using management, quality of service products and tools to achieve better data management, intelligent allocation of storage, and compliance with regulations. Contains information on quality control, looking at SLA (Service Level Agreements), protection by business unit and billing/charge back. Unique insight into hot topics such as next generation bare metal recovery and true system provisioning. This invaluable text will provide system administrators, and database administrators, as well as senior IT managers and decision makers with a thorough understanding of data management and protection. With contributions from Ray Schafer and Paul Mayer.

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Pierre Bonnet Enterprise Data Governance. Reference and Master Data Management Semantic Modeling


In an increasingly digital economy, mastering the quality of data is an increasingly vital yet still, in most organizations, a considerable task. The necessity of better governance and reinforcement of international rules and regulatory or oversight structures (Sarbanes Oxley, Basel II, Solvency II, IAS-IFRS, etc.) imposes on enterprises the need for greater transparency and better traceability of their data. All the stakeholders in a company have a role to play and great benefit to derive from the overall goals here, but will invariably turn towards their IT department in search of the answers. However, the majority of IT systems that have been developed within businesses are overly complex, badly adapted, and in many cases obsolete; these systems have often become a source of data or process fragility for the business. It is in this context that the management of ‘reference and master data’ or Master Data Management (MDM) and semantic modeling can intervene in order to straighten out the management of data in a forward-looking and sustainable manner. This book shows how company executives and IT managers can take these new challenges, as well as the advantages of using reference and master data management, into account in answering questions such as: Which data governance functions are available? How can IT be better aligned with business regulations? What is the return on investment? How can we assess intangible IT assets and data? What are the principles of semantic modeling? What is the MDM technical architecture? In these ways they will be better able to deliver on their responsibilities to their organizations, and position them for growth and robust data management and integrity in the future.

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Jay Etchings A. Strategies in Biomedical Data Science. Driving Force for Innovation


An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

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Группа авторов Management of Data in Clinical Trials


A valuable new edition of the trusted, practical guide to managing data in clinical trials Regardless of size, type, or complexity, accurate results for any clinical trial are ultimately determined by the quality of the collected data. Management of Data in Clinical Trials, Second Edition explores data management and trial organization as the keys to developing an accurate and reliable clinical trial. With a focus on the traditional aspects of data collection as well as recent advances in technology, this new edition provides a complete and accessible guide to the management structure of a clinical trial, from planning and development to design and analysis. Practical approaches that result in the collection of complete and timely data are also provided. While maintaining a comprehensive overview of the knowledge and tools that are essential for the organization of a modern clinical trial, the author has expanded the topical coverage in the Second Edition to reflect the possible uses of recent advances in technology in the data collection process. In addition, the Second Edition discusses the impact of international regulations governing the conduct of clinical trials and provides guidelines on ensuring compliance with national requirements. Newly featured topics include: The growing availability of «off-the-shelf» solutions for clinical trials Potential models for collaboration in the conduct of clinical trials between academia and the pharmaceutical industry The increasing use of the Internet in the collection of data and management of trials Regulatory requirements worldwide and compliance with the ICH Good Clinical Practice (GCP) Guidelines Development of Standard Operating Procedures for the conduct of clinical trials Complete with chapter summaries that reinforce key points as well as over one hundred examples, Management of Data in Clinical Trials, Second Edition is an ideal resource for practitioners in the clinical research community who are involved in the development of clinical trials, including data managers, research associates, data coordinators, physicians, and statisticians. This book also serves as an excellent supplemental text for courses in clinical trials at both the undergraduate and graduate levels.

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Vijay Kumar Fundamentals of Pervasive Information Management Systems


A comprehensive new edition on mobile computing—covering both mobile and sensor data The new paradigm of pervasive computing was born from the needs of highly mobile workers to access and transfer data while on the go. Significant advances in the technology have lent and will continue to lend prevalence to its use—especially in m-commerce. Covering both mobile data and sensor data, this comprehensive text offers updated research on sensor technology, data stream processing, mobile database security, and contextual processing. Packed with cases studies, exercises, and examples, Fundamentals of Pervasive Information Management Systems covers essential aspects of wireless communication and provides a thorough discussion about managing information on mobile database systems (MDS). It addresses the integration of web and workflow with mobile computing and looks at the current state of research. Fundamentals of Pervasive Information Management Systems presents chapters on: Mobile Database System Mobile and Wireless Communication Location and Handoff Management Fundamentals of Database Processing Introduction to Concurrency Control Mechanisms Effect of Mobility on Data Processing Transaction Management in Mobile Database Systems Mobile Database Recovery Wireless Information Dissemination Introduction to Sensor Technology Sensor Technology and Data Streams Management Sensor Network Deployment: Case Studies Fundamentals of Pervasive Information Management Systems is an ideal book for researchers, teachers, and graduate students of mobile computing. The book may also be used as a reference text for researchers or managers.

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Группа авторов Improving data management and decision support systems in agriculture


This collection reviews and summarises the wealth of research on key challenges in developing better data management and decision support systems (DSS) for farmers and examples of how those systems are being deployed to optimise efficiency in crop and livestock production.<br><br>Part 1 reviews general issues underpinning effective decision support systems (DSS) such as data access, standards, tagging and security. Part 2 contains case studies of the practical application of data management and DSS in areas such as crop planting, nutrition and use of rotations, livestock feed and pasture management as well as optimising supply chains for fresh produce.<br><br>With its distinguished editor and international team of authors, Improving data management and decision support systems in agriculture will be a standard reference for researchers in agriculture and computer science interested in improving data management, modelling and decision support systems in farming, as well as government and other agencies supporting the use of precision farming techniques, and companies supplying decision support services to the farming sector.

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Divyakant Agrawal - UCSB

Vice President of Data Solutions and Advertising Systems, ASK.com (January'2006 -- December'2007) Technology Advisor, ASK.com (January'2008 -- December'2008) Technology Advisor, Future Computing Divsion, NEC Japan (January'2008 -- December'2011) Visiting Professor, School of Computer Science, National University of Singapore (June'2010 -- September'2010) William Mong Visiting Research Fellow ...

Divyakant Agrawal - UCSB

Agrawal and El Abbadi used advanced data mining techniques on click-stream data to for detecting this type of fraud. More recently, Drs. Agrawal and El Abbadi's research group has developed a sophisticated data-driven technique for detecting coalition of fraudulent attacks in advertising networks. Unlike the prior approaches, this problem is so complex that its solution requires offline ...

Divyakant AGRAWAL | University of California, Santa ...

Divyakant AGRAWAL of University of California, Santa Barbara, CA (UCSB) | Read 402 publications | Contact Divyakant AGRAWAL

Divyakant Agrawal | UCSB Computer Science

Dr. Divyakant Agrawal is a Professor of Computer Science and the Director of Engineering Computing Infrastructure at the University of California at Santa Barbara. His research expertise is in the areas of database systems, distributed computing, data warehousing, and large-scale information systems. During his professional career, Dr. Agrawal has served on numerous Program Committees of ...

Divyakant Agrawal - Santa Barbara, California ...

View Divyakant Agrawal’s professional profile on LinkedIn. LinkedIn is the world's largest business network, helping professionals like Divyakant Agrawal discover inside connections to ...

Divyakant AGRAWAL - Home

The Challenges of Global-scale Data Management. Faisal Nawab. University of California, Santa Barbara, Santa Barbara, CA, USA, Divyakant Agrawal. University of California, Santa Barbara, Santa Barbara, CA, USA , Amr El Abbadi. University of California, Santa Barbara, Santa Barbara, CA, USA. June 2016 SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data https://doi ...

Divyakant Agrawal | IEE | UC Santa Barbara

Divyakant Agrawal's long-term research activities have been in the area of designing and developing innovative solutions, systems, and algorithms for large-scale systems such as databases, transaction processing systems, data warehouses, and digital libraries. His current activities are focused on energy-efficient designs of algorithms and solutions for data-intensive

Divyakant Agrawal | College of Engineering - UC Santa Barbara

Agrawal’s research focus is on: design and development of large-scale distributed, scalable, and reliable data management platforms for storing, managing, and analyzing big data. His research group has developed innovative storage architectures in the cloud that provide transactional and strong consistency guarantees for managing big data.

Divyakant Agrawal - Home

Minimizing Commit Latency of Transactions in Geo-Replicated Data Stores. Faisal Nawab. UC Santa Barbara, Santa Barbara, USA, Vaibhav Arora. UC Santa Barbara, Santa Barbara, USA, Divyakant Agrawal. US Santa Barbara, Santa Barbara, USA , Amr El Abbadi. UC Santa Barbara, Santa Barbara, USA. May 2015 SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data ...

Divyakant Agrawal - The Mathematics Genealogy Project

According to our current on-line database, Divyakant Agrawal has 3 students and 16 descendants. We welcome any additional information. If you have additional information or corrections regarding this mathematician, please use the update form.To submit students of this mathematician, please use the new data form, noting this mathematician's MGP ID of 30020 for the advisor ID.

Big Data and Cloud Computing: Current State and Future ...

Big Data and Cloud Computing: Current State and Future Opportunities ∗ Divyakant Agrawal Sudipto Das Amr El Abbadi Department of Computer Science University of California, Santa Barbara Santa Barbara, CA 93106-5110, USA {agrawal, sudipto, amr}@cs.ucsb.edu ABSTRACT Scalable database management systems (DBMS)—both for update

SBBD 2016 – Agrawal

Divyakant Agrawal is a Professor of Computer Science at the University of California at Santa Barbara. His research expertise is in the areas of database systems, distributed computing, data warehousing, and large-scale information systems. Divy Agrawal has had visiting appointments at IBM Research, NEC Research, ASK.com, Qatar Computing Research Institute, National University of Singapore ...

Sudipto Das - DBLP

Shoji Nishimura, Sudipto Das, Divyakant Agrawal, Amr El Abbadi: $\mathcal{MD}$ -HBase: design and implementation of an elastic data infrastructure for cloud-scale location services. Distributed Parallel Databases 31 ( 2 ) : 289-319 ( 2013 )

Yueji Yang - DBLP

Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.

A Review of Big Data Management, Benefits and Challenges

Big Data is relatively a new concept which refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.

Divyakant Agrawal | Semantic Scholar

Semantic Scholar profile for Divyakant Agrawal, with 1162 highly influential citations and 489 scientific research papers.

Data Management in the Cloud: Challenges and Opportunities ...

Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an increase in the scale of the data generated as well as consumed by such applications.

Divyakant Agrawal | Semantic Scholar

Semantic Scholar profile for Divyakant Agrawal, with 83 highly influential citations and 59 scientific research papers.

Prof. Divyakant Agrawal, Big Data, Deep Learning, and other Allegories

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Divyakant Agrawal - researchr alias

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Secure and Privacy-Preserving Data Services in the Cloud ...

A Data Centric View ∗ Divyakant Agrawal Amr El Abbadi Shiyuan Wang Department of Computer Science, UC Santa Barbara Santa Barbara, CA 93106-5110, USA {agrawal, amr, sywang}@cs.ucsb.edu ABSTRACT Cloud computing becomes a successful paradigm for data computing and storage. Increasing concerns about data se-curity and privacy in the cloud, however, have emerged. En-suring security and privacy ...

"Challenges and Opportunities with Big Data 2011-1" by ...

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.Heterogeneity, scale, timeliness ...

Divyakant Agrawal – SBBD

Divyakant Agrawal is a Professor of Computer Science at the University of California at Santa Barbara. His research expertise is in the areas of database systems, distributed computing, data warehousing, and large-scale information systems. Divy Agrawal has had visiting appointments at IBM Research, NEC Research, ASK.com, Qatar Computing Research Institute, National University of Singapore ...

‪Divyakant Agrawal‬ - ‪Google Scholar‬

This "Cited by" count includes citations to the following articles in Scholar. The ones marked * may be different from the article in the profile.

Data Management in the Cloud: Challenges and Opportunities ...

Data Management in the Cloud book. Read reviews from world’s largest community for readers. Cloud computing has emerged as a successful paradigm of servi...

Efficient Computation of Frequent and Top-k Elements in ...

Metwally, A., Agrawal, D., El Abbadi, A.: Efficient Computation of Frequent and Top-k Elements in Data Streams. Technical Report 2005-23, University of California, Santa Barbara (September 2005) Google Scholar

Scalable and Elastic Transactional Data Stores for Cloud ...

Scalable and Elastic Transactional Data Stores for Cloud Computing Platforms A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by Sudipto Das Committee in Charge: Professor Divyakant Agrawal, Co-Chair Professor Amr El Abbadi, Co-Chair Dr. Philip A. Bernstein Professor Timothy Sherwood December 2011. The Dissertation ...

Divyakant Agrawal - Publications

Geng Z, Agrawal D, Singal AG, Kircher S, Gupta S. Contained colonic perforation due to cecal retroflexion. World Journal of Gastroenterology. 22: 3285-8. PMID 27004007 DOI: 10.3748/wjg.v22.i11.3285 : 0.36: 2016: Kamal VK, Agrawal D, Pandey RM. Prognostic models for prediction of outcomes after traumatic brain injury based on patients admission ...

Data Management in the Cloud : Divyakant Agrawal ...

Data Management in the Cloud by Divyakant Agrawal, 9781608459247, available at Book Depository with free delivery worldwide.

Data Management in the Cloud: Challenges and Opportunities ...

Data Management in the Cloud: Challenges and Opportunities (Synthesis Lectures on Data Management) by Divyakant Agrawal (2012-12-19) | Divyakant Agrawal; Sudipto Das; Amr El Abbadi | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Challenges and Opportunities with Big Data 2011-1

Divyakant Agrawal, Philip Bernstein, Elisa Bertino, Susan Davidson, Umeshwas Dayal, Michael Franklin, Johannes Gehrke, Laura Haas, Alon Halevy, Jiawei Han, H.V. Jagadish, Alexandros Labrinidis, Sam Madden, Yannis Papakonstantinou, Jignesh Patel, Raghu Ramakrishnan, Kenneth Ross, Cyrus Shahabi, Dan Suciu, Shiv Vaithyanathan, and Jennifer Widom This unpublished paper is available at Purdue e ...

Divyakant Agrawal - sigmod.org

165 Lin Qiao , Divyakant Agrawal, Amr El Abbadi : Supporting Sliding Window Queries for Continuous Data Streams. SSDBM 2003 : 85- 164 Hua-Gang Li , Divyakant Agrawal, Amr El Abbadi , Mirek Riedewald : Exploiting the Multi-Append-Only-Trend Property of Historical Data in Data Warehouses. SSTD 2003 : 179-198

Amazon.com: Data Management in the Cloud: Challenges and ...

Amazon.com: Data Management in the Cloud: Challenges and Opportunities (Synthesis Lectures on Data Management) (9781608459247): Agrawal, Divyakant, Das, Sudipto, El ...

MNS IDM'2000 Project Report Format

Divyakant Agrawal & Amr El Abbadi. Department of Computer Science University of California at Santa Barbara Santa Barbara, CA 93106 Phone: (805) 893-4321 Fax : (805) 893-8553 Email: agrawal@cs.ucsb.edu amr@cs.ucsb.edu. WWW Page. List of Supported Students and Staff (optional) Hae-Don Chon, Research Assistant Kevin O'Gorman, Research Assistant Lin Qiao, Research Assistnat Mirek Riedewald ...

New Frontiers in Information and Software as Services ...

New Frontiers in Information and Software as Services: Service and Application Design Challenges in the Cloud (Lecture Notes in Business Information Processing, Band 74) | Agrawal, Divyakant, Candan, K. Selçuk, Li, Wen-Syan | ISBN: 9783642192937 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

CiteSeerX — The Iterative Data Cube

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): . Data cubes provide aggregate information to support the analysis of the contents of data warehouses and databases. An important tool to analyze data in data cubes is the range query. For range queries that summarize large regions of massive data cubes, computing the query result on-the-fly can result in non ...

Data Management Challenges in Cloud Computing ...

But the growing popularity of “cloud computing”, the resulting shift of a large number of internet applications to the cloud, and the quest towards providing data management services in the cloud, has opened up the challenge for designing data management systems that provide consistency guarantees at a granularity larger than single rows and keys. In this paper, we analyze the design ...

DBLP: Amr El Abbadi

2009; 249 : Sudipto Das, Shyam Antony, Divyakant Agrawal, Amr El Abbadi: CoTS: A Scalable Framework for Parallelizing Frequency Counting over Data Streams. ICDE 2009: 1323-1326: 248 : Divyakant Agrawal, Amr El Abbadi, Fatih Emekçi, Ahmed Metwally: Database Management as a Service: Challenges and Opportunities. ICDE 2009: 1709-1716: 247 : Shiyuan Wang, Jun'ichi Tatemura, Arsany Sawires, Oliver ...

Divyakant Agrawal | DeepAI

Divyakant Agrawal is this you? claim profile. 0 followers The Regents of the University of California Featured Co-authors ... Large scale data management systems utilize State Machine Replication to... 06/18/2019 ∙ by Mohammad Javad Amiri, et al. ∙ 0 ∙ share read it. Towards Global Asset Management in Blockchain Systems ...

WS13: Seminar NOSQL Databases - Georg-August-Universität ...

MapReduce: Simplified Data Processing on Large Clusters. OSDI 2004: 137-150 +Shoji Nishimura, Sudipto Das, Divyakant Agrawal, Amr El Abbadi. MD-HBase: design and implementation of an elastic data infrastructure for cloud-scale location services. Distributed and Parallel Databases 31(2): 289-319 (2013) +Sudipto Das, Divyakant Agrawal, Amr El Abbadi.

Divyakant Agrawal | BibSonomy

Divyakant Agrawal search. Toggle navigation Toggle navigation . sign in; register; home; groups; popular . posts; tags; authors; concepts; discussions; genealogy; sign in; register × Login. Log in with your username. @ I've lost my password. sign in. Log in with your OpenID-Provider. Yahoo! Other OpenID-Provider; sign in. authors; Divyakant Agrawal × Publication title. Copy citation to your ...

Divyakant Agrawal & Sudipto Das Data Management in the ...

Divyakant Agrawal & Sudipto Das Data Management in the Cloud Challenges and Opportunities. Support . Adobe DRM. Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also ...

Data Management in the Cloud: Challenges and Opportunities ...

Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an...

ElasTraS: An Elastic Transactional Data Store in the Cloud

ElasTraS: An Elastic Transactional Data Store in the Cloud Sudipto Das Divyakant Agrawal Amr El Abbadi Department of Computer Science, UC Santa Barbara, CA, USA {sudipto, agrawal, amr}@cs.ucsb.edu Abstract Over the last couple of years, “Cloud Computing” or “Elastic Computing” has emerged as a compelling and successful paradigm for internet scale computing. One of the major ...

New Frontiers in Information and Software as Services ...

Service consumers also expect process and data security, 24/7 service availability, and compliance with privacy regulations. This book focuses on such challenges associated with the design, implementation, deployment, and management of data and software as a service. The 12 papers presented in this volume were contributed by leaders in academia and industry, and were reviewed and supervised by ...

Author: Divyakant Agrawal | Interaction Design Foundation

Divyakant Agrawal: Publications, bio, bibliography, etc. Wen-Syan Li 9 K. Selcuk Candan 17 Amr El Abbadi 18 Publications. Metwally, Ahmed, Agrawal, Divyakant, Abbadi, Amr El (2007): Detectives: detecting coalition hit inflation attacks in advertising networks streams.In: Proceedings of the 2007 International Conference on the World Wide Web, 2007, . pp. 241-250.

Database Systems for Advanced Applications

Indexing and Querying Constantly Evolving Data Using Time Series Analysis Yuni Xia, Sunil Prabhakar, Jianzhong Sun, Shan Lei 637 Mining Generalized Spatio-Temporal Patterns Junmei Wang, Wynne Hsu, Mong Li Lee 649 Exploiting Temporal Correlation in Temporal Data Warehouses Ying Feng, Hua-Gang Li, Divyakant Agrawal, Amr El Abbadi 662 Semantics

Efficient, Consistent and Secure Global-Scale Data Management

Divyakant Agrawal* Amr El Abbadi* *UC Santa Barbara, **SAP, yUC Santa Cruz *fsujaya maiyya, victorzakhary, agrawal, elabbadig@ucsb.edu, **fcetin08@gmail.comg yffnawab@ucsc.edug Abstract—Processing and analyzing data is becoming increas- ingly ubiquitous and is the driving force behind the sustained growth of Internet applications and the emergence of Big Data Analytics. These applications ...

Medians and Beyond: New Aggregation Techniques for Sensor ...

Nisheeth Shrivastava Chiranjeeb Buragohain Divyakant Agrawal ... data values of its children and the other is the total number of its children [17]. In other words, AVERAGEcan be computed by using constant memory and by sending constant sized messages. On the other hand, to answer a MEDIANquery accurately, we need to keep track of all distinct values and thus the message size and memory ...

Bart Baesens Analytics in a Big Data World. The Essential Guide to Data Science and its Applications


The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

3837.13 RUR

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Группа авторов Mobile Database Systems


A breakthrough sourcebook to the challenges and solutions for mobile database systems This text enables readers to effectively manage mobile database systems (MDS) and data dissemination via wireless channels. The author explores the mobile communication platform and analyzes its use in the development of a distributed database management system. Workable solutions for key challenges in wireless information management are presented throughout the text. Following an introductory chapter that includes important milestones in the history and development of mobile data processing, the text provides the information, tools, and resources needed for MDS management, including: * Fundamentals of wireless communication * Location and handoff management * Fundamentals of conventional database management systems and why existing approaches are not adequate for mobile databases * Concurrency control mechanism schemes * Data processing and mobility * Management of transactions * Mobile database recovery schemes * Data dissemination via wireless channels Case studies and examples are used liberally to aid in the understanding and visualization of complex concepts. Various exercises enable readers to test their grasp of each topic before advancing in the text. Each chapter also concludes with a summary of key concepts as well as references for further study. Professionals in the mobile computing industry, particularly e-commerce, will find this text indispensable. With its extensive use of case studies, examples, and exercises, it is also highly recommended as a graduate-level textbook.

12813.49 RUR

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Giudici Paolo Applied Data Mining for Business and Industry


The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

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Olivier Pivert NoSQL Data Models. Trends and Challenges


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It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations.

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The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential – yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. Understand what Big Data is and what it can do Delve into Big Data's specific impact on the insurance industry Learn how advanced analytics can revolutionise the industry Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business – but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data – but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets.

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