th The 18 International Conference on Conceptual Structures (ICCS 2010) was the latest in a series of annual conferences that have been held in Europe, A- tralia, and North America since 1993. The focus of the conference has been the representation and analysis of conceptual knowledge for research and practical application. ICCS brings together researchers and practitioners in information and computer sciences as well as social science to explore novel ways that c- ceptual structures can be deployed. Arising from the research on knowledge representation and reasoning with conceptual graphs, over the years ICCS has broadened its scope to include in- vations from a wider range of theories and related practices, among them other forms of graph-based reasoning systems like RDF or existential graphs, formal concept analysis, Semantic Web technologies, ontologies, concept mapping and more. Accordingly, ICCS represents a family of approaches related to conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, knowledge representation and reasoning, applied mathematics and lattice theory, computational linguistics, conceptual modeling and design, d- grammatic reasoning and logic, intelligent systems and knowledge management. The ICCS 2010 theme "From Information to Intelligence" hints at unve- ing the reasoning capabilities of conceptual structures. Indeed, improvements in storage capacity and performance of computing infrastructure have also - fected the nature of knowledge representation and reasoning (KRR) systems, shifting their focus toward representational power and execution performance. Therefore, KRR research is now faced with a challenge of developing knowledge representation and reasoning structures optimized for such reasonings.

The International Conference on Case-Based Reasoning (ICCBR) is the preeminentinternationalmeetingoncase-basedreasoning(CBR). Through2009, ICCBR (http://www. iccbr. org) had been a biennial conference, held in alter- tion with its sister conference, the European Conference on Case-BasedReas- ing (http://www. eccbr. org), which was located in Europe. At the 2009 ICCBR,the ICCBR ProgramCommittee elected to extend ano?er of consoli- tion with ECCBR. The o?er was accepted by the ECCBR 2010 organizers and they have considered it approved by the ECCBR community, as the two conf- ences share a majority of Program Committee members. ICCBR and ECCBR havebeen theleading conferencesonCBR. From2010,ICCBRandECCBRwill be merged in a single conference series, called ICCBR. As there had been eight previous ICCBR events and nine previous ECCBR events, the combined series is considered the 18th ICCBR. ICCBR 2010 (http://www. iccbr. org/iccbr10) was therefore the 18th in this seriesof internationalconferenceshighlighting the mostsigni?cantcontributions tothe?eldofCBR. TheconferencetookplaceduringJuly19-22,2010inthecity of Alessandria,Italy, on the beautiful campus of the University of Piemonte O- entale "A. Avogadro. " Previous ICCBR conferences were held in Sesimbra, P- tugal(1995),Providence,RhodeIsland,USA(1997),SeeonMonastery,Germany (1999), Vancouver, BC, Canada (2001), Trondheim, Norway (2003), Chicago, Illinois, USA (2005), Belfast, Northern Ireland (2007), and Seattle, Washington, USA (2009). Day 1 of the conference hosted an Applications Track, the second Doctoral Consortium, and the third Computer Cooking Contest. The Applications Track featured ?elded applications and CBR systems demos in industrial and scienti?c settingswithanemphasis ondiscussionandnetworkingbetweenresearchersand industrials.

These are the proceedings of the tenth event of the Industrial Conference on Data Mining ICDM held in Berlin (www.data-mining-forum.de). For this edition the Program Committee received 175 submissions. After the pe- review process, we accepted 49 high-quality papers for oral presentation that are included in this book. The topics range from theoretical aspects of data mining to app- cations of data mining such as on multimedia data, in marketing, finance and telec- munication, in medicine and agriculture, and in process control, industry and society. Extended versions of selected papers will appear in the international journal Trans- tions on Machine Learning and Data Mining (www.ibai-publishing.org/journal/mldm). Ten papers were selected for poster presentations and are published in the ICDM Poster Proceeding Volume by ibai-publishing (www.ibai-publishing.org). In conjunction with ICDM four workshops were held on special hot applicati- oriented topics in data mining: Data Mining in Marketing DMM, Data Mining in LifeScience DMLS, the Workshop on Case-Based Reasoning for Multimedia Data CBR-MD, and the Workshop on Data Mining in Agriculture DMA. The Workshop on Data Mining in Agriculture ran for the first time this year. All workshop papers will be published in the workshop proceedings by ibai-publishing (www.ibai-publishing.org). Selected papers of CBR-MD will be published in a special issue of the international journal Transactions on Case-Based Reasoning (www.ibai-publishing.org/journal/cbr).

This book contains refereed and improved papers presented at the 8th IAPR Workshop on Graphics Recognition (GREC 2009), held in La Rochelle, France, July 22-23, 2009. The GREC workshops provide an excellent opportunity for researchersand practitionersat all levels of experience to meet colleaguesand to share new ideas and knowledge about graphics recognition methods. Graphics recognition is a sub?eld of document image analysis that deals with graphical entities in engineering drawings, sketches, maps, architectural plans, musical scores, mathematical notation, tables, diagrams, etc. GREC 2009 continued the tradition of past workshops held in the Penn State University, USA (GREC 1995, LNCS Volume 1072, Springer Verlag, 1996); Nancy, France (GREC 1997, LNCS Volume 1389, Springer Verlag, 1998); Jaipur, India (GREC 1999, LNCS Volume 1941, Springer Verlag, 2000); Kingston, Canada (GREC 2001, LNCS Volume 2390, Springer Verlag, 2002); Barcelona, Spain (GREC 2003, LNCS Volume 3088, Springer Verlag, 2004); Hong Kong, China (GREC 2005, LNCS Volume 3926, Springer Verlag, 2006); and (GREC 2007, LNCS Volume 5046, Springer Verlag, 2008). The programof GREC 2009 was organized in a single-track 2-day workshop. It comprised several sessions dedicated to speci?c topics. For each session, there was an invited presentation describing the state of the art and stating the open questions for the session's topic, followed by a number of short presentations thatcontributedbyproposingsolutionstosomeofthequestionsorbypresenting results ofthe speaker'swork. Eachsessionwas then concludedby a paneldisc- sion.

th The 15 International Conference on Applications of Natural Language to Information Systems (NLDB 2010) took place during June 23-25 in Cardiff (UK). Since the first edition in 1995, the NLDB conference has been aiming at bringing together resear- ers, people working in industry and potential users interested in various applications of natural language in the database and information system area. However, in order to reflect the growing importance of accessing information from a diverse collection of sources (Web, Databases, Sensors, Cloud) in an equally wide range of contexts (- cluding mobile and tethered), the theme of the 15th International Conference on - plications of Natural Language to Information Systems 2010 was "Communicating with Anything, Anywhere in Natural Language. " Natural languages and databases are core components in the development of inf- mation systems. Natural language processing (NLP) techniques may substantially enhance most phases of the information system lifecycle, starting with requirement analysis, specification and validation, and going up to conflict resolution, result pr- essing and presentation. Furthermore, natural language-based query languages and user interfaces facilitate the access to information for all and allow for new paradigms in the usage of computerized services. Hot topics such as information retrieval (IR), software engineering applications, hidden Markov models, natural language interfaces and semantic networks and graphs imply a complete fusion of databases, IR and NLP techniques.

Simulation and Decision Making, Multi-Agent Applications, Management and e-Business, Mobile Agents and Robots, and Machine Learning. In addition to the main tracks of the symposium there were the following five special sessions: Agent- Based Optimization (ABO2010), Agent-Enabled Social Computing (AESC2010), Digital Economy (DE2010), Using Intelligent Systems for Information Technology Assessment (ISITA2010) and a Doctoral Track. Accepted and presented papers highlight new trends and challenges in agent and multi-agent research. We hope these results will be of value to the research com- nity working in the fields of artificial intelligence, collective computational intel- gence, robotics, machine learning and, in particular, agent and multi-agent systems technologies and applications. We would like to express our sincere thanks to the Honorary Chairs, Romuald Cwilewicz, President of the Gdynia Maritime University, Poland, and Lakhmi C. Jain, University of South Australia, Australia, for their support. Our special thanks go to the Local Organizing Committee chaired by Ireneusz Czarnowski, who did very solid and excellent work. Thanks are due to the Program Co-chairs, all Program and Reviewer Committee members and all the additional - viewers for their valuable efforts in the review process, which helped us to guarantee the highest quality of selected papers for the conference. We cordially thank the - ganizers and chairs of special sessions, which essentially contributed to the success of the conference.

Presented experiments show that usage ofevolutionary approach to feature - duction is justi?ed.Feature selection as well as construction gives goodresults. It is noticeable that attribute construction's best results assign higher classi?- tion accuracy than feature selection alone.That is why, carrying out selection before construction to decrease searchingspace isagoodsolution. Because of indeterministicbehavior of neuralnetworks,it was di?cultto - ducefeaturesetincaseofusingthemto evaluatecandidateresults.Forexample, aneuralnetworklearntverywellondatathatwasdescribedbyfullattributeset, but when thisset was decreased it had huge problems to do this duringrequired number ofepochs.That suggests that usingC4.5 ismuchmore preferred. Numerous experiments havebeen performed and observed.Analysis ofabove results allowsto put the hypothesisthat it is worth to use Construction module as the feature set reduction. But experiments show that Constructormodule does not work sowell whenitusesthe whole initial set offeatures - the search space istoo large.Soit is worth to use ?rstly Selectorand nextConstructor. The second important issue isthatConstructor destructs the semanticmeaning of the features.New constructed features are notunderstandableforusers.In some real-liveproblems measuring offeature values isquite expensive, forsuch problems selector seems to be more suitable because itdiminishes a number of realfeatures.To constructonefeaturesa number ofreal(measured)featurescan be required. Obtainedresults haveencouragedus to extendour system,especiallythe c- structormodule.Weplan to developenlarged set offunctionsFwhich allowsto use the system with data containingdi?erenttype offeatures,not only nume- cal. Such system will be veri?ed usingagreater number ofbenchmark data sets as well as real data. Acknowledgments. This work ispartially ?nanced fromthe Ministryof S- ence and Higher Education Republic of Polandresources in 2008-2010 years as a Poland-Singapore joint research project 65/N-SINGAPORE/2007/0.

The 18th InternationalConference on User Modeling, Adaptation and Person- ization(UMAP 2010)took placeon BigIsland, HawaiiduringJune 20-24,2010. It was the second conference after UMAP 2009 in Trento, Italy, which merged the successful biannual User Modeling (UM) and Adaptive Hypermedia (AH) conference series. The ResearchPapertrackof the conferencewaschairedbyPaulDe Brafrom the Eindhoven University of Technology and Alfred Kobsa from the University ofCalifornia,Irvine. TheywereassistedbyaninternationalProgramCommittee of 80 leading ?gures in the AH and UM communities as well as highly promising youngerresearchers. Papersinthe ResearchPapertrackweregenerallyreviewed by three and sometimes even four reviewers, with one of them acting as a lead who initiates a discussion between reviewers and reconciles their opinions in a meta-review. The conferencesolicitedLong ResearchPapersof up to 12pagesin length, whichrepresentoriginalreports of substantivenew research. In addition, theconferenceacceptedShortResearchPapersofuptosixpagesinlength,whose merit was assessed more in terms of originality and importance than maturity and technical validation. The Research Paper track received 161 submission, with 112 in the long and 49 in the short paper category. Of these, 26 long and 6 short papers were accepted, resulting in an acceptance rate of 23. 2% for long papers and 19. 9% overall. Many authors of rejected papers were encouraged to resubmit to the Poster and Demo track of the conference. Following the example of UMAP 2009, the conference also had an Ind- try Paper track chaired by Bhaskar Mehta from Google, Zur .. ich, Switzerland and Kurt Partridge from PARC, Palo Alto, USA.

The papers contained in this volume were presented at the 21st Annual S- posium on Combinatorial Pattern Matching (CPM 2010) held at NYU-Poly, Brooklyn, New York during June 21-23, 2010. Allthe paperspresentedatthe conferenceareoriginalresearchcontributions. We received 53 submissions from 21 countries. Each paper was reviewed by at least three reviewers. The committee decided to accept 28 papers. The program also includes three invited talks by Zvi Galil from Tel Aviv University, Israel, Richard M. Karp from University of California at Berkeley, USA, and Je?rey S. Vitter from Texas A&M University, USA. The objective of the annual CPM meetings is to provide an international forum for research in combinatorial pattern matching and related applications. It addresses issues of searching and matching strings and more complicated p- terns such as trees, regular expressions, graphs, point sets, and arrays. The goal is to derive non-trivialcombinatorialproperties of suchstructures and to exploit these properties in order to either achieve superior performance for the cor- sponding computational problems or pinpoint conditions under which searches cannot be performed e?ciently. The meeting also deals with problems in c- putational biology, data compression and data mining, coding, information - trieval, natural language processing and pattern recognition. TheAnnual SymposiumonCombinatorialPatternMatchingstartedin 1990, andhassincetakenplaceeveryyear.PreviousCPM meetingswereheld inParis, London, Tucson, Padova, Asilomar, Helsinki, Laguna Beach, Aarhus, Pisc- away, Warwick, Montreal, Jerusalem, Fukuoka, Morelia, Istanbul, Jeju Island, Barcelona, London, Ontario, Pisa, and Lille.

Intelligence and security informatics (ISI) is concerned with the study of the devel- ment and use of advanced information technologies and systems for national, inter- tional, and societal security-related applications. The annual IEEE International Conference series on ISI (http://www. isiconference. org/) was started in 2003. In 2006, the Workshop on ISI (http://isi. se. cuhk. edu. hk/2006/) was held in Singapore in c- junction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), with over 100 contributors and participants from all over the world. This would become the start of a new series of ISI meetings in the Pacific Asia region. PAISI 2007 (http://isi. se. cuhk. edu. hk/2007/) was then held in Chengdu, China. PAISI 2008 (http://isi. se. cuhk. edu. hk/2008/) was held in Taipei, Taiwan, in conjunction with IEEE ISI 2008. PAISI 2009 (http://www. business. hku. hk/paisi/2009/) was held in Bangkok, Thailand, in conjunction with PAKDD 2009. These past ISI conferences and workshops brought together academic researchers, law enforcement and intel- gence experts, information technology consultants and practitioners to discuss their research and practice related to various ISI topics. These topics include ISI data m- agement, data and text mining for ISI applications, terrorism informatics, deception and intent detection, terrorist and criminal social network analysis, public health and bio-security, crime analysis, cyber-infrastructure protection, transportation infrastr- ture security, policy studies and evaluation, information assurance, enterprise risk management, information systems security, among others.

This two volume set LNCS 5981 and LNCS 5982 constitutes the refereed proceedings of the 15th International Conference on Database Systems for Advanced Applications, DASFAA 2010, held in Tsukuba, Japan, in April 2010. The 39 revised full papers and 16 revised short papers presented together with 3 invited keynote papers, 22 demonstration papers, 6 industrial papers, and 2 keynote talks were carefully reviewed and selected from 285 submissions. The papers of the first volume are organized in topical sections on P2P-based technologies, data mining technologies, XML search and matching, graphs, spatial databases, XML technologies, time series and streams, advanced data mining, query processing, Web, sensor networks and communications, information management, as well as communities and Web graphs. The second volume contains contributions related to trajectories and moving objects, skyline queries, privacy and security, data streams, similarity search and event processing, storage and advanced topics, industrial, demo papers, and tutorials and panels.

th CICLing 2010 was the 11 Annual Conference on Intelligent Text Processing and Computational Linguistics. The CICLing conferences provide a wide-scope forum for discussion of the art and craft of natural language processing research as well as the best practices in its applications. This volume contains three invited papers and the regular papers accepted for oral presentation at the conference. The papers accepted for poster pres- tation were published in a special issue of another journal (see information on thewebsite).Since 2001,theproceedingsofCICLingconferenceshavebeen p- lished in Springer's Lecture Notes in Computer Science series, as volumes 2004, 2276, 2588, 2945, 3406, 3878, 4394, 4919, and 5449. The volume is structured into 12 sections: - Lexical Resources - Syntax and Parsing - Word Sense Disambiguation and Named Entity Recognition - Semantics and Dialog - Humor and Emotions - Machine Translation and Multilingualism - Information Extraction - Information Retrieval - Text Categorization and Classi?cation - Plagiarism Detection - Text Summarization - Speech Generation The 2010 event received a record high number of submissions in the - year history of the CICLing series. A total of 271 papers by 565 authors from 47 countriesweresubmittedforevaluationbytheInternationalProgramCommittee (see Tables 1 and 2). This volume contains revised versions of 61 papers, by 152 authors, selected for oral presentation; the acceptance rate was 23%.

We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it. Along the way, you'll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you'll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications. What You Will Learn Understand Spark unified data processing platform How to run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functions Build real-time applications using Spark Structured Streaming Develop intelligent applications with the Spark Machine Learning library Who This Book Is For Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce.

The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization.

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified white box approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: * Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language * Features over 750 chapter exercises, allowing readers to assess their understanding of the new material * Provides a detailed case study that brings together the lessons learned in the book * Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Implement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace. Building the Snowflake Data Cloud helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, velocity, and variety growing at exponential rates, you need tools and techniques to quickly categorize, collate, summarize, and aggregate data. You also need the means to seamlessly distribute to release value. This book shows how Snowflake provides all these things and how to use them to your advantage. The book helps you succeed by delivering faster than you can deliver with legacy products and techniques. You will learn how to leverage what you already know, and what you don't, all applied in a Snowflake Data Cloud context. After reading this book, you will discover and embrace the future where the Data Cloud is central. You will be able to position your organization to take advantage by identifying, adopting, and preparing your tooling for the coming wave of opportunity around sharing and monetizing valuable, corporate data. What You Will Learn Understand why Data Cloud is important to the success of your organization Up-skill and adopt Snowflake, leveraging the benefits of cloud platforms Articulate the Snowflake Marketplace and identify opportunities to monetize data Identify tools and techniques to accelerate integration with Data Cloud Manage data consumption by monitoring and controlling access to datasets Develop data load and transform capabilities for use in future projects Who This Book Is For Solution architects seeking implementation patterns to integrate with a Data Cloud; data warehouse developers looking for tips, tools, and techniques to rapidly deliver data pipelines; sales managers who want to monetize their datasets and understand the opportunities that Data Cloud presents; and anyone who wishes to unlock value contained within their data silos

Data is powerful. It separates leaders from laggards and it drives business disruption, transformation, and reinvention. Today's most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. The horsepower of new tools and technologies have provided more opportunities than ever to harness, integrate, and interact with massive amounts of disparate data for business insights and value - something that will only continue in the era of the Internet of Things. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too. The world of data is changing fast. And, it's becoming more visual. Visual insights are becoming increasingly dominant in information management, and with the reinvigorated role of data visualization, this imperative is a driving force to creating a visual culture of data discovery. The traditional standards of data visualizations are making way for richer, more robust and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger and more diverse and dynamic data, by understanding and embracing our human hardwiring for visual communication and storytelling and properly incorporating key design principles and evolving best practices, we take the next step forward to transform data visualizations from tools into unique visual information assets.

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation.

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.

Build a Career in Data Science is the top guide to help readers get their first data science job, then quickly becoming a senior employee. Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills readers need alongside their technical know-how in order to succeed in the field. Key Features * Creating a portfolio to show off your data science projects * Picking the role that's right for you * Assessing and negotiating an offer * Leaving gracefully and moving up the ladder * Interviews with professional data scientists about their experiences This book is for readers who possess the foundational technical skills of data science, and want to leverage them into a new or better job in the field. About the technology From analyzing drug trials to helping sports teams pick new draftees, data scientists utilize data to tackle the big questions of a business. But despite demand, high competition and big expectations make data science a challenging field for the unprepared to break into and navigate. Alongside their technical skills, the successful data scientist needs to be a master of understanding data projects, adapting to company needs, and managing stakeholders. Jacqueline Nolis is a data science consultant and co-founder of Nolis, LLC, with a PhD in Industrial Engineering. Jacqueline has spent years mentoring junior data scientists on how to work within organizations and grow their careers. Emily Robinson is a senior data scientist at Warby Parker, and holds a Master's in Management. Emily's academic background includes the study of leadership, negotiation, and experiences of underrepresented groups in STEM.