1. Developing a unifying theory of data mining that connects different tasks and approaches could help advance the field by providing a theoretical framework.
2. Scaling data mining methods to handle high dimensional and streaming data at massive scales is challenging due to limitations in current approaches for problems like concept drift.
3. Efficiently mining sequential, time series, and noisy time series data remains an important open problem, particularly for applications like financial and seismic predictions.
Over the past 10 years, research systems have evolved from systems that focused on how to structure and record information on research, to systems capable of allowing significant insights to be derived based upon years of high quality information. In 2015, the maturity of the information now collected within many Current Research Information Systems, and the insights that this can provide is of equal or greater value than the insights that could be gleaned from established externally provided research metrics platforms alone. The ability to intersect these external and internal worlds provides new levels of strategic insight not previously available. With the addition of platforms that track altmetrics, and their ability to connect university publications data with a constant flow of real time attention level metrics, an image of a dynamic network of systems emerges, connected together by ever turning ‘cogs’ pushing and translating information. Add to this, the success of ORCID as pervasive researcher identifier infrastructure, and CASRAI as the emerging social contract for information exchange, and it becomes possible to extend this network back from the systems that track and record research information, through to the platforms through which research knowledge is created. The ‘Mechanics’ of this network of systems is more than just getting the ‘plumbing’ right. As research information moves through the network, its audience and purpose changes, the requirements for contextual metadata can also change. This presentation will explore the lived experience of Research Data Mechanics at Digital Science though illustrating how connections between Figshare, Altmetric, Symplectic Elements, and Dimensions can both enhance research system capability and reduce the burden on researchers, and research administration.
The document discusses instructions for a term paper assignment on data mining case analysis. Students must write a 5-8 page paper analyzing a data mining application domain or technique. They are instructed to describe the data, challenges, goals, users, relevant algorithms, and how data mining is used within the chosen topic. Examples of possible case topics and paper requirements are provided.
Open Science in Research Libraries: Research, Research Integrity and Legal As...Marlon Domingus
Session on RDM and legal aspects at the Erasmus+ Staff Mobility - Knowledge Sharing Open Science in Research Libraries. June 12-16 2017. TU Delft and Erasmus University Rotterdam.
Big data is prevalent in our daily life. Not surprisingly, big data becomes a hot topic discussedby commercial worlds, media, magazines, general publics and elsewhere. From academic point of view, isit a research area of potential worth being explored? Or it is just another hype? Are there only computer orIS related scholars suitable for big data research due to its nature? Or scholars from other research areas are alsosuitable for this subject? This study aims to answer these questions through the use of informetricsapproach and data source form the SSCI Journal database, leveraging informetric‟s robust natures ofquantitative power of analyze information in any form onto the data source of representativeness. This research shows that big data research is at its growth phase with an exponential growth patternsince 2012 and with great potential for years to come. And perhaps surprisingly, computer or IS relateddisciplinesare not on the top 5 research areas fromthis research results. In fact, the top five research disciplinesare more diversified then expected: business economics (#1), Government Law (#2), InformationScience/ Library Science (#3), Social Science (#4) and Computer Science (#5). Scholars from the USuniversities are the most productive in this subject while Asian countries, including Taiwan, are alsovisible. Besides, this study also identifies that big data publications from SSCI journal database during2005-2015 do fit Lotka‟s law. This study contributes tounderstand the current big data research trends and also show the ways toresearchers who are interested to conduct future research in big data regardless of their research backgrounds.
HathiTrust Research Center Secure CommonsBeth Plale
Introduces HTRC secure commons, expanded secure infrastructure and services for text mining of HT digital data. Shows results comparing n-gram discovery using Solr full text index and a framework using mapReduce. Compute time over 1 million digital volumes is 1 day with 1024 cores. Weaknesses of Solr in n-gram identification are explored.
9 Data Mining Challenges From Data Scientists Like YouSalford Systems
The document outlines 9 challenges faced by data scientists: 1) poor quality data issues like dirty, missing, or inadequate data, 2) lack of understanding of data mining techniques, 3) lack of good literature on important topics and techniques, 4) difficulty for academic institutions accessing commercial-grade software at reasonable costs, 5) accommodating data from different sources and formats, 6) updating models constantly with new incoming data for online machine learning, 7) dealing with huge datasets requiring distributed approaches, 8) determining the right questions to ask of the data, and 9) remaining objective and letting the data lead rather than preconceptions.
Recent Trends in Big Data Research
[1] Big data research is becoming increasingly popular as more organizations recognize the competitive advantage of data analytics. However, big data research requires a broad set of skills that many organizations currently lack. [2] As data volumes continue to grow exponentially from sources like IoT, making sense of big data will require integrating data from multiple sources and automating data collection and analysis. [3] Successful big data research will depend on taking an end-to-end approach that considers all aspects of the data lifecycle from collection to insights.
Managing 'Big Data' in the social sciences: the contribution of an analytico-...CILIP MDG
This document discusses managing large datasets in the social sciences. It describes how the UK Data Service curates and provides access to large survey and census data. It explores how classification schemes could help organize and provide subject access to these growing datasets. A pilot project classified datasets using the Universal Decimal Classification scheme and found it efficient and helped visualize subject categories. Overall, carefully chosen knowledge organization tools can help provide multidimensional subject access needed to analyze complex datasets.
Over the past 10 years, research systems have evolved from systems that focused on how to structure and record information on research, to systems capable of allowing significant insights to be derived based upon years of high quality information. In 2015, the maturity of the information now collected within many Current Research Information Systems, and the insights that this can provide is of equal or greater value than the insights that could be gleaned from established externally provided research metrics platforms alone. The ability to intersect these external and internal worlds provides new levels of strategic insight not previously available. With the addition of platforms that track altmetrics, and their ability to connect university publications data with a constant flow of real time attention level metrics, an image of a dynamic network of systems emerges, connected together by ever turning ‘cogs’ pushing and translating information. Add to this, the success of ORCID as pervasive researcher identifier infrastructure, and CASRAI as the emerging social contract for information exchange, and it becomes possible to extend this network back from the systems that track and record research information, through to the platforms through which research knowledge is created. The ‘Mechanics’ of this network of systems is more than just getting the ‘plumbing’ right. As research information moves through the network, its audience and purpose changes, the requirements for contextual metadata can also change. This presentation will explore the lived experience of Research Data Mechanics at Digital Science though illustrating how connections between Figshare, Altmetric, Symplectic Elements, and Dimensions can both enhance research system capability and reduce the burden on researchers, and research administration.
The document discusses instructions for a term paper assignment on data mining case analysis. Students must write a 5-8 page paper analyzing a data mining application domain or technique. They are instructed to describe the data, challenges, goals, users, relevant algorithms, and how data mining is used within the chosen topic. Examples of possible case topics and paper requirements are provided.
Open Science in Research Libraries: Research, Research Integrity and Legal As...Marlon Domingus
Session on RDM and legal aspects at the Erasmus+ Staff Mobility - Knowledge Sharing Open Science in Research Libraries. June 12-16 2017. TU Delft and Erasmus University Rotterdam.
Big data is prevalent in our daily life. Not surprisingly, big data becomes a hot topic discussedby commercial worlds, media, magazines, general publics and elsewhere. From academic point of view, isit a research area of potential worth being explored? Or it is just another hype? Are there only computer orIS related scholars suitable for big data research due to its nature? Or scholars from other research areas are alsosuitable for this subject? This study aims to answer these questions through the use of informetricsapproach and data source form the SSCI Journal database, leveraging informetric‟s robust natures ofquantitative power of analyze information in any form onto the data source of representativeness. This research shows that big data research is at its growth phase with an exponential growth patternsince 2012 and with great potential for years to come. And perhaps surprisingly, computer or IS relateddisciplinesare not on the top 5 research areas fromthis research results. In fact, the top five research disciplinesare more diversified then expected: business economics (#1), Government Law (#2), InformationScience/ Library Science (#3), Social Science (#4) and Computer Science (#5). Scholars from the USuniversities are the most productive in this subject while Asian countries, including Taiwan, are alsovisible. Besides, this study also identifies that big data publications from SSCI journal database during2005-2015 do fit Lotka‟s law. This study contributes tounderstand the current big data research trends and also show the ways toresearchers who are interested to conduct future research in big data regardless of their research backgrounds.
HathiTrust Research Center Secure CommonsBeth Plale
Introduces HTRC secure commons, expanded secure infrastructure and services for text mining of HT digital data. Shows results comparing n-gram discovery using Solr full text index and a framework using mapReduce. Compute time over 1 million digital volumes is 1 day with 1024 cores. Weaknesses of Solr in n-gram identification are explored.
9 Data Mining Challenges From Data Scientists Like YouSalford Systems
The document outlines 9 challenges faced by data scientists: 1) poor quality data issues like dirty, missing, or inadequate data, 2) lack of understanding of data mining techniques, 3) lack of good literature on important topics and techniques, 4) difficulty for academic institutions accessing commercial-grade software at reasonable costs, 5) accommodating data from different sources and formats, 6) updating models constantly with new incoming data for online machine learning, 7) dealing with huge datasets requiring distributed approaches, 8) determining the right questions to ask of the data, and 9) remaining objective and letting the data lead rather than preconceptions.
Recent Trends in Big Data Research
[1] Big data research is becoming increasingly popular as more organizations recognize the competitive advantage of data analytics. However, big data research requires a broad set of skills that many organizations currently lack. [2] As data volumes continue to grow exponentially from sources like IoT, making sense of big data will require integrating data from multiple sources and automating data collection and analysis. [3] Successful big data research will depend on taking an end-to-end approach that considers all aspects of the data lifecycle from collection to insights.
Managing 'Big Data' in the social sciences: the contribution of an analytico-...CILIP MDG
This document discusses managing large datasets in the social sciences. It describes how the UK Data Service curates and provides access to large survey and census data. It explores how classification schemes could help organize and provide subject access to these growing datasets. A pilot project classified datasets using the Universal Decimal Classification scheme and found it efficient and helped visualize subject categories. Overall, carefully chosen knowledge organization tools can help provide multidimensional subject access needed to analyze complex datasets.
This document discusses research challenges in data mining for science and engineering. It covers challenges related to information network analysis, discovery and understanding of patterns from data, stream data mining, and other topics. Key points discussed include analyzing complex networks in scientific domains, developing methods for mining long and approximate patterns, and designing algorithms that can handle streaming data.
The document discusses the University of Virginia School of Data Science (SDS) and opportunities for collaboration with NASA. It provides an overview of SDS, including its mission to be a leader in responsible data science through interdisciplinary collaboration. It describes SDS's data science framework, research areas, capabilities, and recent growth. Examples of current research projects involving NASA data on environmental monitoring and forest ecosystems are presented. The document promotes further partnership between SDS and NASA on challenges in science, medicine, and other domains.
International Collaboration Networks in the Emerging (Big) Data Sciencedatasciencekorea
This document summarizes research on international collaboration networks in emerging big data science. It finds that while global scientific collaboration is widespread, collaboration specifically in big data research is still relatively limited. The United States, Germany, United Kingdom, France, and other developed countries form the most central hubs in the big data collaboration network. The study aims to build on previous descriptive analyses by applying social network analysis and examining collaboration patterns and trends over time.
Data science remains a high-touch activity, especially in life, physical, and social sciences. Data management and manipulation tasks consume too much bandwidth: Specialized tools and technologies are difficult to use together, issues of scale persist despite the Cambrian explosion of big data systems, and public data sources (including the scientific literature itself) suffer curation and quality problems.
Together, these problems motivate a research agenda around “human-data interaction:” understanding and optimizing how people use and share quantitative information.
I’ll describe some of our ongoing work in this area at the University of Washington eScience Institute.
In the context of the Myria project, we're building a big data "polystore" system that can hide the idiosyncrasies of specialized systems behind a common interface without sacrificing performance. In scientific data curation, we are automatically correcting metadata errors in public data repositories with cooperative machine learning approaches. In the Viziometrics project, we are mining patterns of visual information in the scientific literature using machine vision, machine learning, and graph analytics. In the VizDeck and Voyager projects, we are developing automatic visualization recommendation techniques. In graph analytics, we are working on parallelizing best-of-breed graph clustering algorithms to handle multi-billion-edge graphs.
The common thread in these projects is the goal of democratizing data science techniques, especially in the sciences.
Web Mining for an Academic Portal: The case of Al-Imam Muhammad Ibn Saud Isla...IOSR Journals
This document discusses using web mining techniques like association rule mining to build an academic portal for Al-Imam Muhammad Ibn Saud Islamic University. It proposes building an information system where web data mining and semantic web technologies are applied using association rule algorithms. This would allow building ontologies for new knowledge and classifying that knowledge to add to composed knowledge databases. The paper examines using techniques like association rule mining on web server logs and document contents and structures to extract patterns and associate web pages and documents. This could help build a semantic portal and retrieve integrated information through the portal.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
The document discusses future developments in cognitive-based knowledge acquisition systems using big data. It covers preparing students and the cognitive landscape for big data analytics through tools like concept maps and visualization. It also addresses challenges like determining where information comes from, whether humans or computers can best identify patterns in data, and whether autonomous systems will eventually replace human decision making.
The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
From Text to Data to the World: The Future of Knowledge GraphsPaul Groth
Keynote Integrative Bioinformatics 2018
https://docs.google.com/document/d/1E7D4_CS0vlldEcEuknXjEnSBZSZCJvbI5w1FdFh-gG4/edit
Can we improve research productivity through providing answers stemming from knowledge graphs? In this presentation, I discuss different ways of building and combining knowledge graphs.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
This document discusses challenges in managing large amounts of scientific data from various sources like experiments, simulations, literature, and archives. It proposes making all scientific data available online to increase scientific information sharing and productivity. Key steps discussed are data ingest, organization, modeling, integration with literature, documentation, curation and long-term preservation. The cloud is presented as a way to provide scalable access and analysis of large datasets.
The document discusses opportunities for collaboration between the University of Virginia School of Data Science (SDS) and NASA. It provides an overview of SDS, including its mission to be a leader in responsible data science through interdisciplinary collaboration and societal benefit. Examples are given of current SDS research projects involving NASA data on climate change and forest ecosystems. The document proposes areas for potential SDS-NASA collaboration such as courses involving NASA content, funded research projects, student fellowships and faculty positions. It aims to leverage the strengths of both organizations in responsible data science.
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
Presentation of our paper at the WHISE workshop at ESWC 2016 on requirements for metadata over non-public datasets for the science & technology studies field.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Mining Big Data using Genetic AlgorithmIRJET Journal
This document discusses using genetic algorithms to mine big data through clustering. It begins by introducing big data and the challenges of analyzing large and complex data sets using traditional methods. It then proposes using a combination of genetic algorithms and existing clustering algorithms to more efficiently process big data. Specifically, it suggests genetic algorithms can optimize clustering results for big data by combining advantages of genetic algorithms and clustering. The document provides an overview of concepts like data mining, genetic algorithms and big data, and how genetic algorithms may be applied to clustering large data sets.
This document discusses the need for benchmarking and evaluation of visualization tools for data mining. It proposes developing standardized test datasets and metrics to compare different visualization approaches. The challenges include:
1) Performance depends on user expertise - domain knowledge is needed to understand complex real-world datasets. Evaluations must account for different user skill levels.
2) Perceptual issues - comparisons require controlling display/viewing conditions and ensuring users receive comparable training to learn how to interpret visualizations.
3) Acceptance by the KDD community - overcoming technical and cultural barriers to establishing benchmarking as a standard practice. The document advocates developing a centralized testing laboratory to standardize evaluations.
Los componentes web son objetos dinámicos que se pueden agregar a las páginas web para añadir funcionalidades como contadores de visitas, galerías de fotos y formularios de búsqueda. FrontPage incluye una variedad de componentes web preconstruidos que permiten incluir efectos dinámicos, hojas de cálculo, gráficos y contenido de otras páginas sin necesidad de programación. Algunos componentes requieren extensiones de servidor adicionales para funcionar.
Este documento describe un proyecto escolar que tiene como objetivo valorar la importancia de conocer la historia de Panamá a través de los 500 años del descubrimiento del Mar del Sur y su influencia en el Canal de Panamá. El proyecto utilizará diversas herramientas tecnológicas como Google Drive, PowerPoint y Toondoo para investigar, analizar y difundir información sobre estos importantes hitos históricos con el fin de aumentar la conciencia pública sobre la historia del país.
This document discusses research challenges in data mining for science and engineering. It covers challenges related to information network analysis, discovery and understanding of patterns from data, stream data mining, and other topics. Key points discussed include analyzing complex networks in scientific domains, developing methods for mining long and approximate patterns, and designing algorithms that can handle streaming data.
The document discusses the University of Virginia School of Data Science (SDS) and opportunities for collaboration with NASA. It provides an overview of SDS, including its mission to be a leader in responsible data science through interdisciplinary collaboration. It describes SDS's data science framework, research areas, capabilities, and recent growth. Examples of current research projects involving NASA data on environmental monitoring and forest ecosystems are presented. The document promotes further partnership between SDS and NASA on challenges in science, medicine, and other domains.
International Collaboration Networks in the Emerging (Big) Data Sciencedatasciencekorea
This document summarizes research on international collaboration networks in emerging big data science. It finds that while global scientific collaboration is widespread, collaboration specifically in big data research is still relatively limited. The United States, Germany, United Kingdom, France, and other developed countries form the most central hubs in the big data collaboration network. The study aims to build on previous descriptive analyses by applying social network analysis and examining collaboration patterns and trends over time.
Data science remains a high-touch activity, especially in life, physical, and social sciences. Data management and manipulation tasks consume too much bandwidth: Specialized tools and technologies are difficult to use together, issues of scale persist despite the Cambrian explosion of big data systems, and public data sources (including the scientific literature itself) suffer curation and quality problems.
Together, these problems motivate a research agenda around “human-data interaction:” understanding and optimizing how people use and share quantitative information.
I’ll describe some of our ongoing work in this area at the University of Washington eScience Institute.
In the context of the Myria project, we're building a big data "polystore" system that can hide the idiosyncrasies of specialized systems behind a common interface without sacrificing performance. In scientific data curation, we are automatically correcting metadata errors in public data repositories with cooperative machine learning approaches. In the Viziometrics project, we are mining patterns of visual information in the scientific literature using machine vision, machine learning, and graph analytics. In the VizDeck and Voyager projects, we are developing automatic visualization recommendation techniques. In graph analytics, we are working on parallelizing best-of-breed graph clustering algorithms to handle multi-billion-edge graphs.
The common thread in these projects is the goal of democratizing data science techniques, especially in the sciences.
Web Mining for an Academic Portal: The case of Al-Imam Muhammad Ibn Saud Isla...IOSR Journals
This document discusses using web mining techniques like association rule mining to build an academic portal for Al-Imam Muhammad Ibn Saud Islamic University. It proposes building an information system where web data mining and semantic web technologies are applied using association rule algorithms. This would allow building ontologies for new knowledge and classifying that knowledge to add to composed knowledge databases. The paper examines using techniques like association rule mining on web server logs and document contents and structures to extract patterns and associate web pages and documents. This could help build a semantic portal and retrieve integrated information through the portal.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
The document discusses future developments in cognitive-based knowledge acquisition systems using big data. It covers preparing students and the cognitive landscape for big data analytics through tools like concept maps and visualization. It also addresses challenges like determining where information comes from, whether humans or computers can best identify patterns in data, and whether autonomous systems will eventually replace human decision making.
The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
From Text to Data to the World: The Future of Knowledge GraphsPaul Groth
Keynote Integrative Bioinformatics 2018
https://docs.google.com/document/d/1E7D4_CS0vlldEcEuknXjEnSBZSZCJvbI5w1FdFh-gG4/edit
Can we improve research productivity through providing answers stemming from knowledge graphs? In this presentation, I discuss different ways of building and combining knowledge graphs.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
This document discusses challenges in managing large amounts of scientific data from various sources like experiments, simulations, literature, and archives. It proposes making all scientific data available online to increase scientific information sharing and productivity. Key steps discussed are data ingest, organization, modeling, integration with literature, documentation, curation and long-term preservation. The cloud is presented as a way to provide scalable access and analysis of large datasets.
The document discusses opportunities for collaboration between the University of Virginia School of Data Science (SDS) and NASA. It provides an overview of SDS, including its mission to be a leader in responsible data science through interdisciplinary collaboration and societal benefit. Examples are given of current SDS research projects involving NASA data on climate change and forest ecosystems. The document proposes areas for potential SDS-NASA collaboration such as courses involving NASA content, funded research projects, student fellowships and faculty positions. It aims to leverage the strengths of both organizations in responsible data science.
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
Presentation of our paper at the WHISE workshop at ESWC 2016 on requirements for metadata over non-public datasets for the science & technology studies field.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Mining Big Data using Genetic AlgorithmIRJET Journal
This document discusses using genetic algorithms to mine big data through clustering. It begins by introducing big data and the challenges of analyzing large and complex data sets using traditional methods. It then proposes using a combination of genetic algorithms and existing clustering algorithms to more efficiently process big data. Specifically, it suggests genetic algorithms can optimize clustering results for big data by combining advantages of genetic algorithms and clustering. The document provides an overview of concepts like data mining, genetic algorithms and big data, and how genetic algorithms may be applied to clustering large data sets.
This document discusses the need for benchmarking and evaluation of visualization tools for data mining. It proposes developing standardized test datasets and metrics to compare different visualization approaches. The challenges include:
1) Performance depends on user expertise - domain knowledge is needed to understand complex real-world datasets. Evaluations must account for different user skill levels.
2) Perceptual issues - comparisons require controlling display/viewing conditions and ensuring users receive comparable training to learn how to interpret visualizations.
3) Acceptance by the KDD community - overcoming technical and cultural barriers to establishing benchmarking as a standard practice. The document advocates developing a centralized testing laboratory to standardize evaluations.
Los componentes web son objetos dinámicos que se pueden agregar a las páginas web para añadir funcionalidades como contadores de visitas, galerías de fotos y formularios de búsqueda. FrontPage incluye una variedad de componentes web preconstruidos que permiten incluir efectos dinámicos, hojas de cálculo, gráficos y contenido de otras páginas sin necesidad de programación. Algunos componentes requieren extensiones de servidor adicionales para funcionar.
Este documento describe un proyecto escolar que tiene como objetivo valorar la importancia de conocer la historia de Panamá a través de los 500 años del descubrimiento del Mar del Sur y su influencia en el Canal de Panamá. El proyecto utilizará diversas herramientas tecnológicas como Google Drive, PowerPoint y Toondoo para investigar, analizar y difundir información sobre estos importantes hitos históricos con el fin de aumentar la conciencia pública sobre la historia del país.
GroundTruth Initiative helps communities use digital tools for greater representation in development and democracy. It started the Map Kibera pilot project in 2009 to map the Kibera slum in Nairobi, Kenya, which had previously been left off maps. GroundTruth has since expanded its work to other locations, using data and storytelling to give voice to communities. In 2013, it was seeking partnerships to collaborate on projects involving open data, citizen engagement, citizen media, and mapping methodologies.
This document appears to be a survey conducted at the Kaunas Vanda Tumeniene rehabilitation centre school in January 2012 that assessed students' attitudes and behaviors related to environmental issues. The survey found that most students care about the environment and nature, and many take actions like recycling at home and school. While recycling of some materials like paper and organic waste is common, recycling of other materials like batteries, metal, and clothes is less frequent. The survey also examined the school's waste management and efforts to save energy, water, and materials. It found that while the school encourages recycling, it could improve by providing more specific containers for different waste types and better communicating recycling procedures.
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Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
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based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
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01Introduction to data mining chapter 1.pptadmsoyadm4
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated extraction of meaningful patterns from large data sets, and notes it draws on techniques from machine learning, statistics, pattern recognition, and database systems. The chapter outlines different types of data that can be mined, patterns that can be discovered, and applications of data mining in various domains including business, science, and on the web.
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated extraction of meaningful patterns from large data sets, and notes it draws on techniques from machine learning, statistics, pattern recognition, and database systems. The chapter outlines different types of data that can be mined, patterns that can be discovered, and applications of data mining in various domains including business, science, and on the web.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining has become important due to the massive growth of digital data. Data mining aims to extract useful patterns from large datasets through techniques like generalization, association analysis, classification, and cluster analysis. It can be applied to many types of data and has uses in domains such as business, science, and healthcare to help analyze data and discover useful knowledge.
This document provides an overview of data mining concepts and techniques from the third edition of the textbook "Data Mining: Concepts and Techniques" by Jiawei Han, Micheline Kamber, and Jian Pei. It introduces why data mining is important due to the massive growth of data, defines data mining, and discusses the multi-dimensional nature of data mining including the types of data, patterns, techniques and applications. The chapter also covers data mining functions such as generalization, association analysis, classification, and cluster analysis.
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Data Mining Xuequn Shang NorthWestern Polytechnical Universitybutest
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Unit 1 (Chapter-1) on data mining concepts.pptPadmajaLaksh
This document provides an introduction to data mining concepts. It discusses why data mining is important due to the massive growth of data. It defines data mining as the automated analysis of large datasets to discover hidden patterns and unknown correlations. The document presents a multi-dimensional view of data mining, including the types of data that can be mined, the patterns that can be discovered, techniques used, and applications. It provides an overview of the key concepts in data mining.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
10 problems 06
1. December 8, 2006 13:28 WSPC/173-IJITDM 00225
International Journal of Information Technology & Decision Making
Vol. 5, No. 4 (2006) 597–604
c World Scientific Publishing Company
10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH
QIANG YANG
Department of Computer Science
Hong Kong University of Science and Technology
Clearwater Bay, Kowloon, Hong Kong, China
XINDONG WU
Department of Computer Science
University of Vermont
33 Colchester Avenue, Burlington, Vermont 05405, USA
xwu@cs.uvm.edu
CONTRIBUTORS: PEDRO DOMINGOS, CHARLES ELKAN, JOHANNES GEHRKE,
JIAWEI HAN, DAVID HECKERMAN, DANIEL KEIM, JIMING LIU,
DAVID MADIGAN, GREGORY PIATETSKY-SHAPIRO, VIJAY V. RAGHAVAN,
RAJEEV RASTOGI, SALVATORE J. STOLFO,
ALEXANDER TUZHILIN and BENJAMIN W. WAH
In October 2005, we took an initiative to identify 10 challenging problems in data mining
research, by consulting some of the most active researchers in data mining and machine
learning for their opinions on what are considered important and worthy topics for future
research in data mining. We hope their insights will inspire new research efforts, and
give young researchers (including PhD students) a high-level guideline as to where the
hot problems are located in data mining.
Due to the limited amount of time, we were only able to send out our survey requests
to the organizers of the IEEE ICDM and ACM KDD conferences, and we received an
overwhelming response. We are very grateful for the contributions provided by these
researchers despite their busy schedules. This short article serves to summarize the 10
most challenging problems of the 14 responses we have received from this survey. The
order of the listing does not reflect their level of importance.
Keywords: Data mining; machine learning; knowledge discovery.
1. Developing a Unifying Theory of Data Mining
Several respondents feel that the current state of the art of data mining research
is too “ad-hoc.” Many techniques are designed for individual problems, such as
classification or clustering, but there is no unifying theory. However, a theoretical
framework that unifies different data mining tasks including clustering, classifica-
tion, association rules, etc., as well as different data mining approaches (such as
statistics, machine learning, database systems, etc.), would help the field and pro-
vide a basis for future research.
597
2. December 8, 2006 13:28 WSPC/173-IJITDM 00225
598 Q. Yang & X. Wu
There is also an opportunity and need for data mining researchers to solve some
longstanding problems in statistical research, such as the age-old problem of avoid-
ing spurious correlations. This is sometimes related to the problem of mining for
“deep knowledge,” which is the hidden cause for many observations. For example, it
was found that in Hong Kong, there is a strong correlation between the timing of TV
series by one particular star and the occurrences of small market crashes in Hong
Kong. However, to conclude that there is a hidden cause behind the correlation is
too rash. Another example is: can we discover Newton’s laws from observing the
movements of objects?
2. Scaling Up for High Dimensional Data and High Speed
Data Streams
One challenge is how to design classifiers to handle ultra-high dimensional classifica-
tion problems. There is a strong need now to build useful classifiers with hundreds of
millions or billions of features, for applications such as text mining and drug safety
analysis. Such problems often begin with tens of thousands of features and also with
interactions between the features, so the number of implied features gets huge quickly.
One important problem is mining data streams in extremely large databases
(e.g. 100 TB). Satellite and computer network data can easily be of this scale.
However, today’s data mining technology is still too slow to handle data of this
scale. In addition, data mining should be a continuous, online process, rather than
an occasional one-shot process. Organizations that can do this will have a decisive
advantage over ones that do not. Data streams present a new challenge for data
mining researchers.
One particular instance is from high speed network traffic where one hopes
to mine information for various purposes, including identifying anomalous events
possibly indicating attacks of one kind or another. A technical problem is how to
compute models over streaming data, which accommodate changing environments
from which the data are drawn. This is the problem of “concept drift” or “envi-
ronment drift.” This problem is particularly hard in the context of large streaming
data. How may one compute models that are accurate and useful very efficiently?
For example, one cannot presume to have a great deal of computing power and
resources to store a lot of data, or to pass over the data multiple times. Hence,
incremental mining and effective model updating to maintain accurate modeling of
the current stream are both very hard problems.
Data streams can also come from sensor networks and RFID applications. In
the future, RFIDs will be a huge area, and analysis of this data is crucial to its
success.
3. Mining Sequence Data and Time Series Data
Sequential and time series data mining remains an important problem. Despite
progress in other related fields, how to efficiently cluster, classify and predict the
trends of these data is still an important open topic.
3. December 8, 2006 13:28 WSPC/173-IJITDM 00225
10 Challenging Problems in Data Mining Research 599
A particularly challenging problem is the noise in time series data. It is an impor-
tant open issue to tackle. Many time series used for predictions are contaminated
by noise, making it difficult to do accurate short-term and long-term predictions.
Examples of these applications include the predictions of financial time series and
seismic time series. Although signal processing techniques, such as wavelet anal-
ysis and filtering, can be applied to remove the noise, they often introduce lags
in the filtered data. Such lags reduce the accuracy of predictions because the pre-
dictor must overcome the lags before it can predict into the future. Existing data
mining methods also have difficulty in handling noisy data and learning meaningful
information from the data.
Some of the key issues that need to be addressed in the design of a practical
data miner for noisy time series include:
• Information/search agents to get information: Use of wrong, too many, or too
little search criteria; possibly inconsistent information from many sources; seman-
tic analysis of (meta-) information; assimilation of information into inputs to
predictor agents.
• Learner/miner to modify information selection criteria: apportioning of biases to
feedback; developing rules for Search Agents to collect information; developing
rules for Information Agents to assimilate information.
• Predictor agents to predict trends: Incorporation of qualitative information; multi-
objective optimization not in closed form.
4. Mining Complex Knowledge from Complex Data
One important type of complex knowledge is in the form of graphs. Recent research
has touched on the topic of discovering graphs and structured patterns from large
data, but clearly, more needs to be done.
Another form of complexity is from data that are non-i.i.d. (independent and
identically distributed). This problem can occur when mining data from multiple
relations. In most domains, the objects of interest are not independent of each other,
and are not of a single type. We need data mining systems that can soundly mine
the rich structure of relations among objects, such as interlinked Web pages, social
networks, metabolic networks in the cell, etc.
Yet another important problem is how to mine non-relational data. A great
majority of most organizations’ data is in text form, not databases, and in more
complex data formats including Image, Multimedia, and Web data. Thus, there is
a need to study data mining methods that go beyond classification and clustering.
Some interesting questions include how to perform better automatic summarization
of text and how to recognize the movement of objects and people from Web and
Wireless data logs in order to discover useful spatial and temporal knowledge.
There is now a strong need for integrating data mining and knowledge inference.
It is an important future topic. In particular, one important area is to incorporate
background knowledge into data mining. The biggest gap between what data mining
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systems can do today and what we’d like them to do is that they’re unable to relate
the results of mining to the real-world decisions they affect — all they can do is
hand the results back to the user. Doing these inferences, and thus automating the
whole data mining loop, requires representing and using world knowledge within the
system. One important application of the integration is to inject domain information
and business knowledge into the knowledge discovery process.
Related to mining complex knowledge, the topic of mining interesting knowledge
remains important. In the past, several researchers have tackled this problem from
different angles, but we still do not have a very good understanding of what makes
discovered patterns “interesting” from the end-user perspective.
5. Data Mining in a Network Setting
5.1. Community and social networks
Today’s world is interconnected through many types of links. These links include
Web pages, blogs, and emails. Many respondents consider community mining and
the mining of social networks as important topics. Community structures are impor-
tant properties of social networks. The identification problem in itself is a chal-
lenging one. First, it’s critical to have the right characterization of the notion of
“community” that is to be detected. Second, the entities/nodes involved are dis-
tributed in real-life applications, and hence distributed means of identification will
be desired. Third, a snapshot-based dataset may not be able to capture the real
picture; what is most important lies in the local relationships (e.g. the nature and
frequency of local interactions) between the entities/nodes. Under these circum-
stances, our challenge is to understand (1) the network’s static structures (e.g.
topologies and clusters) and (2) dynamic behavior (such as growth factors, robust-
ness, and functional efficiency). A similar challenge exists in bio-informatics, as we
are currently moving our attention to the dynamic studies of regulatory networks.
A questions related to this issue is what local algorithms/protocols are necessary
in order to detect (or form) communities in a bottom-up fashion (as in the real
world).
A concrete question is as follows. Email exchanges within an organization or in
one’s own mailbox over a long period of time can be mined to show how various
networks of common practice or friendship start to emerge. How can we obtain and
mine useful knowledge from them?
5.2. Mining in and for computer networks — high-speed mining
of high-speed streams
Network mining problems pose a key challenge. Network links are increasing in
speed, and service providers are now deploying 1 Gig Ethernet and 10 Gig Ethernet
link speeds. To be able to detect anomalies (e.g. sudden traffic spikes due to a DoS
(Denial of Service) attack or catastrophic event), service providers will need to be
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able to capture IP packets at high link speeds and also analyze massive amounts
(several hundred GB) of data each day. One will need highly scalable solutions here.
Good algorithms are, therefore, needed to detect whether DoS attacks do not
exist. Also, once an attack has been detected, how does one discriminate between
legitimate traffic and attack traffic so that it is possible to drop attack packets? We
need techniques to
(1) detect DoS attacks,
(2) trace back to find out who the attackers are, and
(3) drop those packets that belong to attack traffic.
6. Distributed Data Mining and Mining Multi-Agent Data
The problem of distributed data mining is very important in network problems. In
a distributed environment (such as a sensor or IP network), one has distributed
probes placed at strategic locations within the network. The problem here is to
be able to correlate the data seen at the various probes, and discover patterns in
the global data seen at all the different probes. There could be different models
of distributed data mining here, but one could involve a NOC that collects data
from the distributed sites, and another in which all sites are treated equally. The
goal here obviously would be to minimize the amount of data shipped between the
various sites — essentially, to reduce the communication overhead.
In distributed mining, one problem is how to mine across multiple heterogeneous
data sources: multi-database and multi-relational mining.
Another important new area is adversary data mining. In a growing number of
domains — email spam, counter-terrorism, intrusion detection/computer security,
click spam, search engine spam, surveillance, fraud detection, shopbots, file sharing,
etc. — data mining systems face adversaries that deliberately manipulate the data
to sabotage them (e.g. make them produce false negatives). We need to develop
systems that explicitly take this into account, by combining data mining with game
theory.
7. Data Mining for Biological and Environmental Problems
Many researchers that we surveyed believe that mining biological data continues
to be an extremely important problem, both for data mining research and for
biomedical sciences. An example of a research issue is how to apply data mining to
HIV vaccine design. In molecular biology, many complex data mining tasks exist,
which cannot be handled by standard data mining algorithms. These problems
involve many different aspects, such as DNA, chemical properties, 3D structures,
and functional properties.
There is also a need to go beyond bio-data mining. Data mining researchers
should consider ecological and environmental informatics. One of the biggest
concerns today, which is going to require significant data mining efforts, is the
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question of how we can best understand and hence utilize our natural environ-
ment and resources — since the world today is highly “resource-driven”! Data
mining will be able to make a high impact in the area of integrated data fusion
and mining in ecological/environmental applications, especially when involving
distributed/decentralized data sources, e.g. autonomous mobile sensor networks
for monitoring climate and/or vegetation changes.
For example, how can data mining technologies be used to study and find out
contributing factors in the observed doubling of the number of hurricane occurrences
over the past decades, as recently reported in Science magazine? Most of the data
sources that we are dealing with today are fast evolving, e.g. those from stock
markets or city traffic. There is much interesting knowledge yet to be discovered, as
far as the dynamic change regularities and/or their cross-interactions are concerned.
In this regard, one of the challenges today is how to deal with the problem of
dynamic temporal behavioral pattern identification and prediction in: (1) very large-
scale systems (e.g. global climate changes and potential “bird flu” epidemics) and
(2) human-centered systems (e.g. user-adapted human-computer interaction or P2P
transactions).
Related to these questions about important applications, there is a need to focus
on “killer applications” of data mining. So far three important and challenging
applications for data mining have emerged: bioinformatics, CRM/personalization
and security applications. However, more explorations are needed to expand these
applications and extend the list of applications.
8. Data Mining Process-Related Problems
Important topics exist in improving data-mining tools and processes through
automation, as suggested by several researchers. Specific issues include how to auto-
mate the composition of data mining operations and building a methodology into
data mining systems to help users avoid many data mining mistakes. If we automate
the different data mining process operations, it would be possible to reduce human
labor as much as possible. One important issue is how to automate data cleaning.
We can build models and find patterns very fast today, but 90 percent of the cost
is in pre-processing (data integration, data cleaning, etc.) Reducing this cost will
have a much greater payoff than further reducing the cost of model-building and
pattern-finding. Another issue is how to perform systematic documentation of data
cleaning. Another issue is how to combine visual interactive and automatic data
mining techniques together. He observes that in many applications, data mining
goals and tasks cannot be fully specified, especially in exploratory data analy-
sis. Visualization helps to learn more about the data and define/refine the data
mining tasks.
There is also a need for the development of a theory behind interactive explo-
ration of large/complex datasets. An important question to ask is: what are the
compositional approaches for multi-step mining “queries”? What is the canonical
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set of data mining operators for the interactive exploration approach? For example,
the data mining system Clementine has a nice user interface, but what is the theory
behind its operations?
9. Security, Privacy, and Data Integrity
Several researchers considered privacy protection in data mining as an important
topic. That is, how to ensure the users’ privacy while their data are being mined.
Related to this topic is data mining for protection of security and privacy. One
respondent states that if we do not solve the privacy issue, data mining will become
a derogatory term to the general public.
Some respondents consider the problem of knowledge integrity assessment to be
important. We quote their observations: “Data mining algorithms are frequently
applied to data that have been intentionally modified from their original version,
in order to misinform the recipients of the data or to counter privacy and secu-
rity threats. Such modifications can distort, to an unknown extent, the knowledge
contained in the original data. As a result, one of the challenges facing researchers
is the development of measures not only to evaluate the knowledge integrity of
a collection of data, but also of measures to evaluate the knowledge integrity of
individual patterns. Additionally, the problem of knowledge integrity assessment
presents several challenges.”
Related to the knowledge integrity assessment issue, the two most significant
challenges are: (1) develop efficient algorithms for comparing the knowledge con-
tents of the two (before and after) versions of the data, and (2) develop algorithms
for estimating the impact that certain modifications of the data have on the statis-
tical significance of individual patterns obtainable by broad classes of data mining
algorithms. The first challenge requires the development of efficient algorithms and
data structures to evaluate the knowledge integrity of a collection of data. The
second challenge is to develop algorithms to measure the impact that the modifica-
tion of data values has on a discovered pattern’s statistical significance, although
it might be infeasible to develop a global measure for all data mining algorithms.
10. Dealing with Non-Static, Unbalanced and Cost-Sensitive Data
An important issue is that the learned models should incorporate time because
data is not static and is constantly changing in many domains. Historical actions
in sampling and model building are not optimal, but they are not chosen randomly
either. This gives the following challenging phenomenon for the data collection
process. Suppose that we use the data collected in 2000 to learn a model. We then
apply this model to select inside the 2001 population. Subsequently, we use the data
about the individuals selected in 2001 to learn a new model, and then apply this
model in 2002. If this process continues, then each time a new model is learned, its
training set has been created using a different selection bias. Thus, a challenging
problem is how to correct the bias as much as possible.
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Another related issue is how to deal with unbalanced and cost-sensitive data, a
major challenge in research. Charles Elkan made the observation in an invited talk
at ICML 2003 Workshop on Learning from Imbalanced Data Sets. First, in previous
studies, it has been observed that UCI datasets are small and not highly unbalanced.
In a typical real-world dataset, there are at least 105 examples and 102.5 features,
without single well-defined target class. Interesting cases have a frequency of less
than 0.01. There is much information on costs and benefits, but no overall model of
profit and loss. There are different cost matrices for different examples. However,
most cost matrix entries are unknown. An example of this dataset is the direct
marketing DMEF data library. Furthermore, the costs of different outcomes are
dependent on the examples; for example, the false negative cost of direct marketing
is directly proportional to the amount of a potential donation. Traditional methods
for obtaining these costs relied on sampling methods. However, sampling methods
can easily give biased results.
11. Conclusions
Since its conception in the late 1980s, data mining has achieved tremendous success.
Many new problems have emerged and have been solved by data mining researchers.
However, there is still a lack of timely exchange of important topics in the commu-
nity as a whole. This article summarizes a survey that we have conducted to rank
10 most important problems in data mining research. These problems are sampled
from a small, albeit important, segment of the community. The list should obviously
be a function of time for this dynamic field.
Finally, we summarize the 10 problems below:
• Developing a unifying theory of data mining
• Scaling up for high dimensional data and high speed data streams
• Mining sequence data and time series data
• Mining complex knowledge from complex data
• Data mining in a network setting
• Distributed data mining and mining multi-agent data
• Data mining for biological and environmental problems
• Data Mining process-related problems
• Security, privacy and data integrity
• Dealing with non-static, unbalanced and cost-sensitive data
Acknowledgments
We thank all who have responded to our survey requests despite their busy
schedules. We wish to thank Pedro Domingos, Charles Elkan, Johannes Gehrke,
Jiawei Han, David Heckerman, Daniel Keim, Jiming Liu, David Madigan, Gregory
Piatetsky-Shapiro, Vijay V. Raghavan, and his associates, Rajeev Rastogi, Salva-
tore J. Stolfo, Alexander Tuzhilin, and Benjamin W. Wah for their kind input.