{"id":1183,"date":"2014-06-21T15:53:48","date_gmt":"2014-06-21T15:53:48","guid":{"rendered":"http:\/\/wp.sigmod.org\/?p=1183"},"modified":"2017-02-13T14:06:43","modified_gmt":"2017-02-13T14:06:43","slug":"data-exploration-new-name-for-an-old-hat","status":"publish","type":"post","link":"https:\/\/wp.sigmod.org\/?p=1183","title":{"rendered":"Exploratory search: New name for an old hat?"},"content":{"rendered":"\n\n\n\n
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Exploratory search has gained prominence in recent years. There is an increased interest from several scientific communities, from the information retrieval and database to human-computer interaction and visualization communities, in moving beyond the traditional query-browse-refine model supported by web search engines and database systems alike, and towards support for human intelligence amplification and information understanding. Exploratory search systems should inherently support symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes [Whi 09<\/a>]. In this post, four researchers set to explore exploratory search from different angles and study the emerging needs and objectives as well as the challenges and problems that need to be tackled.<\/p>\n

Exploratory Search in Structured Data: Data Warehouses and OLAP to the Rescue? <\/h3>\n

by Melanie Herschel<\/b><\/p>\n

Looking more closely at what problems the field of exploratory search encompasses, Marchionini has established two main aspects of exploratory search that go beyond the classical problem of looking up information using classical search mechanisms [Mar 06<\/a>]. More specifically, exploratory search has the general goal of (i<\/em>) learning, thus acquiring new knowledge and (ii<\/em>) investigating to possibly reveal new facts. Sub-tasks to achieve these goals include: <\/p>\n

Learning<\/strong>: knowledge acquisition, comprehension and interpretation, comparison, aggregation and integration, and socialization. <\/p>\n

Investigating<\/strong>: Accretion, analysis, synthesis, evaluation, discovery, planning and forecasting, and transformation.<\/p>\n

Clearly, exploratory search applies to both structured and unstructured data, as we want to leverage as much data as possible in the above tasks. However, if we focus on structured data, we notice that quite a few of the tasks mentioned above resemble those for which data warehouses and online analytical processing (OLAP) have been developed. One example of a learning task supported by data warehouses is integration<\/em>, as one of the main purposes of data warehouses is to integrate data from distributed and heterogeneous sources in a single repository. Consequently, data comparison<\/em> is easily supported as well. As for supporting investigation tasks, OLAP and data mining algorithms readily support analysis of data stored in a data warehouse, and reports synthesize such data. Finally, data warehouses are deployed to support decision making, such as planning<\/em> and forecasting<\/em>. <\/p>\n

So are the problems of exploratory search already solved for structured data through data warehouses and OLAP? <\/p>\n

To some extent yes but there are still many areas of exploratory search that data warehouses do not cover, such as socialization, comprehension and interpretation, and discovery. In the following, I will highlight three main areas where I believe exploratory search will require novel approaches to reach its full potential. <\/p>\n

Discovery<\/strong>. Data warehouses are designed based on a predefined information demand, meaning that the analytical queries they support are fixed in advance and for sure cannot go beyond queries that the schema of the data warehouse supports. Essentially, the data warehouse is designed to efficiently answer predefined queries. Opposed to that, in exploratory search, the information demand is unknown a priori, the goal of discovery being to also reveal things that users of the system did not think about during design time and thus are not covered by the rigid schema. Hence, system design for exploratory search needs to plan for the unplanned. Making an analogy to farming, we can say that whereas data warehouses and OLAP are useful for harvesting what has been planted, exploratory search is all about exploring and laboring new land<\/em>. <\/p>\n

Adaptation<\/strong>. Given the rigid cage schemas impose on a data warehouse, these systems typically have limited capabilities for changing or evolving information needs. But these are the essence of exploratory search, as learning is an iterative process that requires a search-refine-expand paradigm. That is, whereas data warehouses allow us to freely move in a cage, exploratory search systems need to be able to adapt to a new and rapidly changing environment<\/em>. <\/p>\n

Data and Users<\/strong>. As a final distinction here, we have limited the discussion above to structured data, but obviously, a wealth of information resides in unstructured data that exploratory search should natively support. This brings us back to a long going discussion on integrating databases and information retrieval. Additionally, many limitations of the data warehouse come from the rigid schema, so could maybe NoSQL databases be of help here? Finally, data warehouses and OLAP are targeted towards expert users that know their domain, whereas exploratory search is for the masses and human-computer interaction will be key to success<\/em>. <\/p>\n

To conclude, we have seen that data warehouses may support to some extent exploratory search, but they have serious limitations when it comes to discovery, adaptability, and the support of all types of relevant data and users.<\/p>\n

Exploratory Search and Web searching<\/h3>\n

by Yannis Tzitzikas<\/b><\/p>\n

Web searching is probably the most frequent user task in the Web, during which users typically get back a linear list of hits. In fact, web search engines are very good in ranking, and there is a plethora of ranking methods for capturing various aspects (topic relevance, authority, popularity, credibility and personalization). However, ranking is not enough. Ranking is adequate for focalized (else called precision-oriented) information needs, however the majority of information needs are recall-oriented <\/em> (according to various user studies). For recall-oriented information needs, the user wants to find and understand more than one search hit. Examples include bibliographic survey writing, medical information seeking, patent search, hotel booking, and car buying. In general, recall-oriented information needs have an exploratory nature and aim at decision making (based on one or more criteria). To support such information needs, it is beneficial to provide methods that:\n<\/p>\n

– enable easy access to low ranked hits,<\/p>\n

– allow browsing the relevant hits and resources in groups (according to various criteria),<\/p>\n

– offer overviews (of variable complexity) of the hits, and<\/p>\n

– let the user to restrict gradually the search results.<\/p>\n

The last years we, in the University of Crete and FORTH-ICS, have been investigating methods that can complement the classical Web searching with functionality for exploratory search. At first, we developed the web search engine (WSE) Mitos, a system which offers faceted search over the results of the submitted queries. Specifically, it supports facets corresponding to metadata attributes of the web pages (static metadata), as well as facets corresponding to the outcome of snippet-based clustering algorithms (a kind of dynamic metadata). The user can then restrict her focus gradually, by interacting with the resulting multidimensional structure through simple clicks. <\/p>\n

The system IOS [Faf 12<\/a>] was developed next, providing analogous features but during query typing (type-ahead search). This functionality is offered for the frequent queries and takes advantage of novel partitioned trie-based indexes for reducing the increased main memory requirements. Subsequently, through XSearch [Faf 12b<\/a>] we investigated and showed how the available Linked Open Data (LOD) can be exploited for offering facets based on the outcome of entity mining techniques. Again, this approach is applied at query time over the textual snippets of the search hits, where LOD provides information about the names of the entities of interest. This approach has been proved successful for professional search, specifically in the domain of patent search and marine search. With X-ENS [Faf 12c<\/a>] we focused on configurability, i.e., allowing the end user to configure the entities of interest, and also browse the information that LOD provides for these entities.<\/p>\n

However, to offer entity mining not over the snippets, but over the full contents of the search hits, more than one machines are needed for downloading and analyzing the full contents of the hits. A recent work shows how MapReduce can be used for this purpose [Kit 14<\/a>]. <\/p>\n

Subsequently, we investigated how we could enable the user to easily express preferences, simple and complicated ones, over the elements the user interacts with. Such preferences can affect the ordering of the different aspects of the multidimensional (and hierarchically organized) information space. We have developed the prototype system Hippalus that realized this approach over a proposed preference framework [Pap 14<\/a>].<\/p>\n

More information about the aforementioned systems is available here<\/a>. <\/p>\n

Based on our experience, we could identify four main topics that we believe current IR\/Web search does not cover, and exploratory search will require to reach its full potential:<\/p>\n

Ubiquity<\/strong>. Faceted browsing of search results and gradual restriction should be possible for any kind of query, for any domain, and with no predetermined facets. In other words, methods that bypass the need for explicit configuration (regarding facets, entity types, LOD sources) are required.<\/p>\n

Fusion of Structured and Unstructured Content<\/strong>. The exploitation of LOD in the exploratory search process is promising, e.g. for Named Entity Recognition and disambiguation. However, the fusion of structured and unstructured content requires more work.<\/p>\n

User Control<\/strong>. Explicit, user-provided, and controllable preference management is beneficial for supporting a transparent decision making process. We believe that the framework supported by the Hippalus system is a first step towards this direction.<\/p>\n

Evaluation<\/strong>. We need easy-to-follow methods for evaluating the effectiveness of exploratory search methods, and easily reproducible evaluation results. Although the classical IR has well established methodologies for evaluation, things are not so clear and straightforward in interactive IR (IIR).<\/p>\n

Exploratory Search and Multimedia Data<\/h3>\n

by K. Selcuk Candan<\/b><\/p>\n

\nBy its very nature, multimedia data exploration shares the 3V challenges ([V]olume, [V]elocity, and[V]ariety ) of the so called \u201cBig Data\u201d applications. Systems supporting multimedia data exploration, however, must tackle additional, more specific, challenges, including those posed by the [H]igh-dimensional<\/em>, [M]ulti-modal<\/em> (temporal, spatial, hierarchical, and graph-structured), and inter-[L]inked<\/em> nature of most multimedia data as well as the [I]mprecision<\/em> of the media features and [S]parsity<\/em> of the observations in the real-world. <\/p>\n

Moreover, since the end-users for most multimedia data exploration tasks are us (i.e., humans), we need to consider fundamental constraints posed by [H]uman<\/em> beings, from the difficulties they face in providing unambiguous specifications of interest or preference, subjectivity in their interpretations of results, and their limitations in perception and memory. Last, but not the least, since a large portion of multimedia data is human-centered, we also need to account for the users’ (and others’) needs for [P]rivacy<\/em>.<\/p>\n

Multimedia exploration (on data with the above characteristics and by users with the above limitations) is an inherently dynamic process, and systems for multimedia exploration must be able to support, efficiently and effectively, a continuous exploration cycle involving four key steps:<\/p>\n

sense & integrate<\/strong>: the system takes as inputs and integrates data, media, and models of the application space and continuously sensed real-time media data, <\/p>\n

filter, rank & recommend<\/strong>: the system provides support for context-aware access to integrated media data sets, <\/p>\n

visualize & feedback<\/strong>: the system acquires accurate user feedback through an intuitive data and result representation, and<\/p>\n

act & adapt<\/strong>: the system provides continuous adaptation of models of data, context, and user preference based on user feedback. <\/p>\n

Naturally, each and every step of this multimedia exploration cycle poses significant challenges. While it would be impossible to enumerate all the challenges, I would like to identify the following five \u201ccore\u201d challenges: <\/p>\n

– media annotation, summarization, and (dimensionality) reduction, <\/p>\n

– user, community, context, preference modeling and feedback, <\/p>\n

– multi-modal and richly structured\/linked data exploration, <\/p>\n

– dynamic\/evolving multimedia data exploration, and of course <\/p>\n

– bridging the semantic gap in media exploration. <\/p>\n

Unfortunately, while as a community we have done great advances in tackling these core challenges, we probably have to admit that we are still quite far from addressing any of these issues satisfactorily, especially within the context of large multimedia data collections. In fact, at least in the short- to medium-term future, these five issues will continue to form the core challenges in multimedia exploration and retrieval. <\/p>\n

If there is one thing that is becoming more urgent, however, it is that the ever-increasing scale and the speed of the data implies that to support the above media exploration cycle, our emphasis must shift towards development of integrated data platforms that can support, in an optimized and scalable manner, both media analysis (feature extraction, clustering, partitioning aggregation, summarization, classification, latent analysis) and data manipulation (filtering, integration, personalized and task-oriented retrieval) operations. <\/p>\n

Personalizing Data Exploration: Exploring the Past<\/h3>\n

by Amelie Marian<\/b><\/p>\n

\nPersonal data is now pervasive as digital devices are capturing every part of our lives. Data is constantly collected and saved by users, either voluntarily in files, emails, social media interactions, multimedia objects, calendar items, contacts, etc., or passively by various applications such as GPS tracking of mobile devices, records of utility usage, financial transactions, or quantified self sensors. A typical user will have data recorded on many devices, cloud services, and proprietary systems; access to the data can be difficult because of the data fragmentation, security issues, or practical concerns.<\/p>\n

Companies and organizations have famously been able to take advantage of the wealth of information produced by individuals, learning patterns and visualizing trends on a large number of data points produced by many users. Yet, individual uses do not have accessible tools to retrieve, manage and analyze their own data. <\/p>\n

Leveraging personal data is critical to many data exploration tasks:<\/p>\n

Exploring our past<\/strong>. A specific type of data exploration is re-finding [Tee 04<\/a>], [Ber 08<\/a>], whose goal is to find information that has been created, received, or seen by the user. Unlike traditional web search or exploration tasks whose focus is usually on discovering new information\/documents, a re-finding exploration task has a target object it is attempting to recover.<\/p>\n

Fragmentation of data<\/em> is a main challenge. Users rarely own and store their personal data, with the exception of personal files. Most personal information is stored in the cloud by commercial companies that may offer some limited access, usually via a web browser or an API, to a user\u2019s personal data. Attempting to retrieve and cross-reference personal information then leads to a tedious, often maddening, process of individually accessing all the relevant sources of data and manually linking their information. For example, checking that an insurance claim was correctly processed may require looking at a calendar application to find the doctor\u2019s appointment date, checking the claim status on the insurance web site, and consulting a bank web portal to confirm that the payment was received.<\/p>\n

The future of personal data exploration lies in the creation of personal data exploration tools, in the spirit of Dataspaces [Blu 07<\/a>], [Hal 06<\/a>], that will integrate personal data from a variety of sources, and allow users to visualize and search through their digital memories based on any piece of information they remember, following threads of information to navigate from one memory to the next.<\/p>\n

Exploring our social data<\/strong>. Personal data is not limited to a user\u2019s own data production. In an increasingly connected world, what other users in our social network share with us is also relevant to our data exploration. Users looking for book recommendations may want to explore their social network connections for books they have read or discussed. Some desktop search systems, such as deskWeb [Zer 10<\/a>], have integrated the user\u2019s social network graph, expanding the searched data set to include information available in the social network.<\/p>\n

Personalizing our data exploration<\/strong>. Users have individual habits and patterns, as well as different types of data, context and information needs. By inferring knowledge from their past personal information and their interactions with their data, we can personalize data exploration and fit it to the users’ individual needs. <\/p>\n

Social network information can be leveraged to learn contextual information about a user and guide data exploration. For instance, \u201cStudent Center\u201d has different meaning for different groups of users. Knowing which social group the user belongs to can help us identify entities, e.g., observing that a majority of the user’s friends go to Rutgers University and attend events at \u201cBusch Campus Student Center, Rutgers University”, increases the likelihood that the phrase \u201cStudent Center\u201d in the user\u2019s data and queries refers to that particular student center. Similarly, past queries can be used to learn some information about the type of the entities present in the personal data, or about the personal context.<\/p>\n

This type of personalized data exploration is gaining traction in prospective memory applications, such as Google Now, which focus on reminding users of their appointments and to-do items. <\/p>\n

Overall, understanding and leveraging personal data is crucial for many data exploration tasks, either because the exploration is on the personal information itself, or because personal information can give significant insights and directions for traditional data exploration needs.<\/p>\n

References<\/a> <\/strong>
\n
\n[Whi 09] \tRyen W. White and Resa A. Roth. Exploratory Search: Beyond the Query-Response Paradigm. Synthesis Lectures on Information Concepts, Retrieval, and Services, 2009, Vol. 1, No. 1 , Pages 1-98
\n<\/span>
\n[Mar 06] \tGary Marchionini. Exploratory search: from finding to understanding. Communications of the ACM. Volume 49 Issue 4, April 2006, Pages 41-46.
\n<\/span>
\n[Faf 12] \tPavlos Fafalios, Ioannis Kitsos, Yannis Tzitzikas: Scalable, flexible and generic instant overview search. WWW 2012: 333-336.
\n<\/span>
\n[Faf 12b] \tPavlos Fafalios, Ioannis Kitsos, Yannis Marketakis, Claudio Baldassarre, Michail Salampasis, Yannis Tzitzikas: Web Searching with Entity Mining at Query Time. IRFC 2012: 73-88.
\n<\/span>
\n[Faf 12c] \tPavlos Fafalios, Yannis Tzitzikas: X-ENS: semantic enrichment of web search results at real-time. SIGIR 2013: 1089-1090.
\n<\/span>
\n[Pap 14] \tPanagiotis Papadakos, Yannis Tzitzikas: Hippalus: Preference-enriched Faceted Exploration. EDBT\/ICDT Workshops 2014: 167-172.
\n<\/span>
\n[Kit 14] \tIoannis Kitsos, Kostas Magoutis, Yannis Tzitzikas, Scalable entity-based summarization of web search results using MapReduce, Journal on Distributed and Parallel Databases, 32(3), 2014, 405-446.
\n<\/span>
\n[Tee 04] \tJaime Teevan, Christine Alvarado, Mark S. Ackerman, and David R. Karger. The perfect search engine is not enough: a study of orienteering behavior in directed search. In\u00a0CHI, pages 415\u2013422, 2004.
\n<\/span>
\n[Ber 08] \tOfer Bergman, Ruth Beyth-Marom, Rafi Nachmias, Noa Gradovitch, and Steve Whittaker. Improved search engines and navigation preference in personal information management.\u00a0ACM Trans. Inf. Syst., 26(4):20:1\u201320:24, October 2008.
\n<\/span>
\n[Blu 07] \tLukas Blunschi, Jens peter Dittrich, Olivier Ren Girard, Shant Kirakos, Karakashian Marcos, and Antonio Vaz Salles. A dataspace odyssey: The imemex personal dataspace management system. In\u00a0CIDR, 2007.
\n<\/span>
\n[Hal 06] \tAlon Halevy, Michael Franklin, and David Maier. Principles of dataspace systems. Communications of the ACM, 2006.\u00a0
\n<\/span>
\n[Zer 10] \tSergej Zerr, Elena Demidova, and Sergey Chernov. deskweb2.0: Combining desktop and social search. In\u00a0Proc. of Desktop Search Workshop, In conjunction with the 33rd Annual International ACM SIGIR 2010, 23 July 2010, Geneva, Switzerland, 2010.
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Blogger Profiles: <\/strong>
\n\"melanie\"<\/a>Melanie Herschel<\/a> is an Associate Professor in the Computer Science Department of the University of Paris South and is also member of the INRIA Oak project. Before this, after receiving her Ph.D. in Computer Science from Humboldt-University Berlin (2008), she was a postdoctoral researcher at IBM Research – Almaden (2008 – 2009) and at the University of T\u00fcbingen, Germany (2009 – 2011). During her research career, she has focused on topics related to data integration, data quality, data provenance, and, more recently, on processing large amounts of Web Data. Melanie participates in several nationally funded research projects and is an active member of the database research community, as she has served on numerous program committees, is a frequent journal referee, and has organized international workshops.
\n<\/em>
\n \"yannisNew\"<\/a>Yannis Tzitzikas<\/a> is Assistant Professor in the Department of Computer Science of the University of Crete and Research Associate of FORTH-ICS. His research interests fall in the intersection of the following areas: Information Systems, Information Indexing and Retrieval, Conceptual Modeling, Knowledge Representation and Reasoning. He is one of the editors (and author of several chapters) of the book \u201cDynamic Taxonomies and Faceted Search\u201d (Springer 2009), he is currently involved in the ongoing EU projects i-Marine, SCIDIP-ES, Aparsen NoW (WP leader), and he is national contact of the MUMIA (Multilingual and Multifaceted Interactive Information Access) Cost Action. His current research revolves around Faceted Interactive Search, and lately on methods that can bridge the world of documents with the world of data at search time. He has published more than 80 papers in refereed international conferences and journals (including ACM Transactions on the Web, VLDB Journal, Journal on Data Semantics, International Semantic Web Conference (winner of best paper award), ESWC, and many others).<\/em>
\n
\n\"candan\"<\/a> K. Sel\u00e7uk Candan<\/a> is a Professor of Computer Science and Engineering at the Arizona State University. He has published over 170 journal and peer-reviewed conference articles, one book on multimedia retrieval, and 16 book chapters. He has 9 patents. Prof. Candan served as an associate editor of one of the most respected database journals, the Very Large Databases (VLDB) journal. He is also in the editorial board of the IEEE Transactions on Multimedia and the Journal of Multimedia. He has served in the organization and program committees of various conferences. In 2006, he served as an organization committee member for SIGMOD\u201906, the flagship database conference of the ACM. In 2008, he served as a PC Chair for another leading, flagship conference of the ACM, this time focusing on multimedia research (MM\u201908). More recently, he served as a program committee group leader for ACM SIGMOD\u201910. He also serves in the review board of the Proceedings of the VLDB Endowment (PVLDB). In 2011, he served as a general co-chair for the ACM MM\u201911 conference. In 2012 he served as a general co-chair for ACM SIGMOD\u201912. He has successfully served as the PI or co-PI of numerous grants, including from the National Science Foundation, Air Force Office of Research, Army Research Office, Mellon Foundation, HP Labs, and JCI. He also served as a Visiting Research Scientist at NEC Laboratories America for over 10 years. He is a member of the Executive Committee of ACM SIGMOD and an ACM Distinguished Scientist.
\n<\/em>
\n \"amelie\"<\/a> Am\u00e9lie Marian<\/a> is an Associate Professor in the Computer Science Department at Rutgers University. Her research interests are in Personal Information Management, Ranked Query Processing, Semi-structured data and Web data Management. Am\u00e9lie received her Ph.D. in Computer Science from Columbia University in 2005. From March 1999 to August 2000, she was a member of the VERSO project at INRIA-Rocquencourt. She is the recipient of a Microsoft Live Labs Award, three Google Research Awards, and an NSF CAREER award.<\/em>
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Copyright @ 2014, Melanie Herschel, Yannis Tzitzikas, K. Sel\u00e7uk Candan, Am\u00e9lie Marian, All rights reserved.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Exploratory search has gained prominence in recent years. There is an increased interest from several scientific communities, from the information retrieval and database to human-computer interaction and visualization communities, in moving beyond the traditional query-browse-refine model supported by web search engines and database systems alike, and towards support for human intelligence amplification and information understanding. […]<\/p>\n","protected":false},"author":16,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"coauthors":[54,51,52,53,56],"class_list":["post-1183","post","type-post","status-publish","format-standard","hentry","category-data-exploration"],"views":1279,"_links":{"self":[{"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=\/wp\/v2\/posts\/1183","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1183"}],"version-history":[{"count":111,"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=\/wp\/v2\/posts\/1183\/revisions"}],"predecessor-version":[{"id":2136,"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=\/wp\/v2\/posts\/1183\/revisions\/2136"}],"wp:attachment":[{"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1183"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1183"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1183"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/wp.sigmod.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcoauthors&post=1183"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}