@article {14415,
	title = {Organizing User Search Histories},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {24},
	year = {2012},
	month = {2012/05//},
	pages = {912 - 925},
	abstract = {Users are increasingly pursuing complex task-oriented goals on the web, such as making travel arrangements, managing finances, or planning purchases. To this end, they usually break down the tasks into a few codependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user{\textquoteright}s historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.},
	keywords = {alteration;query, analysis;user, engines;search, engines;text, goal;textual, group, historical, History, identification;query, information, interfaces;, log;sessionization;task-oriented, organization;query, processing;search, query, query;user, quest;query, ranking;search, search, search;long-term, similarity;time, suggestion;result, threshold;user, Web;collaborative},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2010.251},
	author = {Hwang,Heasoo and Lauw,H. W and Getoor, Lise and Ntoulas,A.}
}
@conference {17369,
	title = {Social discovery framework: building capacity and seeking solutions},
	booktitle = {Proceedings of the 8th ACM conference on Creativity and cognition},
	series = {C\&$\#$38;C {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {307 - 308},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {While journalists often portray discovery as the thrilling insight of a brilliant individual, many discoveries require years of work by competing and collaborating teams. Often large amounts of foundational work are necessary and dialogs among participants help clarify goals. The Social Discovery Framework suggests that (1) there are important processes in building capacity and then seeking solutions and (2) those that initiate requests are often as important as those who seek solutions. The implications of the Social Discovery Framework are that improved social tools to build capacity, initiate requests, and support dialog would accelerate the discovery process as much as the more visible tools for individuals seeking solutions.},
	keywords = {Collaboration, collective intelligence, community, creativity support tools, crowdsourcing, search, social discovery, User interfaces},
	isbn = {978-1-4503-0820-5},
	doi = {10.1145/2069618.2069669},
	url = {http://doi.acm.org/10.1145/2069618.2069669},
	author = {Shneiderman, Ben}
}
@conference {16069,
	title = {Aligning temporal data by sentinel events: discovering patterns in electronic health records},
	booktitle = {Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems},
	series = {CHI {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {457 - 466},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61\% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.},
	keywords = {electronic health record, Evaluation, Information Visualization, search, temporal data, Uncertainty},
	isbn = {978-1-60558-011-1},
	doi = {10.1145/1357054.1357129},
	url = {http://doi.acm.org/10.1145/1357054.1357129},
	author = {Wang,Taowei David and Plaisant, Catherine and Quinn,Alexander J. and Stanchak,Roman and Murphy,Shawn and Shneiderman, Ben}
}
@conference {16423,
	title = {The effect of crossover on the behavior of the GA in dynamic environments: a case study using the shaky ladder hyperplane-defined functions},
	booktitle = {Proceedings of the 8th annual conference on Genetic and evolutionary computation},
	series = {GECCO {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {1289 - 1296},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {One argument as to why the hyperplane-defined functions (hdf{\textquoteright}s) are a good testbed for the genetic algorithm (GA) is that the hdf{\textquoteright}s are built in the same way that the GA works. In this paper we test that hypothesis in a new setting by exploring the GA on a subset of the hdf{\textquoteright}s which are dynamic---the shaky ladder hyperplane-defined functions (sl-hdf{\textquoteright}s). In doing so we gain insight into how the GA makes use of crossover during its traversal of the sl-hdf search space. We begin this paper by explaining the sl-hdf{\textquoteright}s. We then conduct a series of experiments with various crossover rates and various rates of environmental change. Our results show that the GA performs better with than without crossover in dynamic environments. Though these results have been shown on some static functions in the past, they are re-confirmed and expanded here for a new type of function (the hdf) and a new type of environment (dynamic environments). Moreover we show that crossover is even more beneficial in dynamic environments than it is in static environments. We discuss how these results can be used to develop a richer knowledge about the use of building blocks by the GA.},
	keywords = {crossover, dynamic environments, Genetic algorithms, hyperplane-defined functions, search},
	isbn = {1-59593-186-4},
	doi = {10.1145/1143997.1144198},
	url = {http://doi.acm.org/10.1145/1143997.1144198},
	author = {Rand, William and Riolo,Rick and Holland,John H.}
}
@conference {15029,
	title = {Strategies for exploring large scale data},
	booktitle = {Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on},
	year = {2004},
	month = {2004/05//},
	pages = {2 - 2},
	abstract = {We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.},
	keywords = {algorithms;, association;, asymptotic, bounds;, business, data, data;, database, databases;, demographic, discovery;, Indexing, indexing;, information, knowledge, large, linear, mining;, multidimensional, objects;, optimal, Parallel, pattern, processing;, query, querying;, range, scale, scientific, search, serial, series, series;, simulation, space, structure;, structures;, techniques;, temporal, TIME, value, very, window;},
	doi = {10.1109/ISPAN.2004.1300447},
	author = {JaJa, Joseph F.}
}
@conference {16894,
	title = {Depth-first k-nearest neighbor finding using the MaxNearestDist estimator},
	booktitle = {Image Analysis and Processing, 2003.Proceedings. 12th International Conference on},
	year = {2003},
	month = {2003/09//},
	pages = {486 - 491},
	abstract = {Similarity searching is an important task when trying to find patterns in applications which involve mining different types of data such as images, video, time series, text documents, DNA sequences, etc. Similarity searching often reduces to finding the k nearest neighbors to a query object. A description is given of how to use an estimate of the maximum possible distance at which a nearest neighbor can be found to prune the search process in a depth-first branch-and-bound k-nearest neighbor finding algorithm. Using the MaxNearestDist estimator (Larsen, S. and Kanal, L.N., 1986) in the depth-first k-nearest neighbor algorithm provides a middle ground between a pure depth-first and a best-first k-nearest neighbor algorithm.},
	keywords = {branch-and-bound, data, depth-first, distance;, DNA, documents;, estimation;, estimator;, finding;, images;, k-nearest, matching;, maximum, MaxNearestDist, mining;, neighbor, parameter, pattern, possible, process;, processing;, query, search, searching;, sequences;, series;, similarity, text, TIME, tree, video;},
	doi = {10.1109/ICIAP.2003.1234097},
	author = {Samet, Hanan}
}
@conference {14961,
	title = {Efficient techniques for range search queries on earth science data},
	booktitle = {Scientific and Statistical Database Management, 2002. Proceedings. 14th International Conference on},
	year = {2002},
	month = {2002///},
	pages = {142 - 151},
	abstract = {We consider the problem of organizing large scale earth science raster data to efficiently handle queries for identifying regions whose parameters fall within certain range values specified by the queries. This problem seems to be critical to enabling basic data mining tasks such as determining associations between physical phenomena and spatial factors, detecting changes and trends, and content based retrieval. We assume that the input is too large to fit in internal memory and hence focus on data structures and algorithms that minimize the I/O bounds. A new data structure, called a tree-of-regions (ToR), is introduced and involves a combination of an R-tree and efficient representation of regions. It is shown that such a data structure enables the handling of range queries in an optimal I/O time, under certain reasonable assumptions. We also show that updates to the ToR can be handled efficiently. Experimental results for a variety of multi-valued earth science data illustrate the fast execution times of a wide range of queries, as predicted by our theoretical analysis.},
	keywords = {based, computing;, content, data, data;, databases;, Earth, factors;, large, mining, mining;, natural, processing;, queries;, query, range, raster, retrieval;, scale, Science, sciences, search, spatial, structures;, tasks;, temporal, tree, tree-of-regions;, visual},
	doi = {10.1109/SSDM.2002.1029714},
	author = {Shi,Qingmin and JaJa, Joseph F.}
}
@conference {18468,
	title = {Multimodal 3-D tracking and event detection via the particle filter},
	booktitle = {IEEE Workshop on Detection and Recognition of Events in Video, 2001. Proceedings},
	year = {2001},
	month = {2001///},
	pages = {20 - 27},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Determining the occurrence of an event is fundamental to developing systems that can observe and react to them. Often, this determination is based on collecting video and/or audio data and determining the state or location of a tracked object. We use Bayesian inference and the particle filter for tracking moving objects, using both video data obtained from multiple cameras and audio data obtained using arrays of microphones. The algorithms developed are applied to determining events arising in two fields of application. In the first, the behavior of a flying echo locating bat as it approaches a moving prey is studied, and the events of search, approach and capture are detected. In a second application we describe detection of turn-taking in a conversation between possibly moving participants recorded using a smart video conferencing setup},
	keywords = {algorithms, APPROACH, audio data collection, audio signal processing, Bayesian inference, Bayesian methods, belief networks, CAMERAS, capture, conversation, echo, Educational institutions, Event detection, event occurrence, filtering theory, flying echo locating bat behaviour, Image motion analysis, inference mechanisms, Laboratories, microphone arrays, moving object tracking, moving participants, moving prey, multimodal 3D tracking, multiple cameras, Object detection, particle filter, Particle filters, Particle tracking, Robustness, search, smart video conferencing setup, target tracking, Teleconferencing, tracking filters, turn-taking detection, video data collection, video signal processing},
	isbn = {0-7695-1293-3},
	doi = {10.1109/EVENT.2001.938862},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {16195,
	title = {Bringing treasures to the surface: iterative design for the Library of Congress National Digital Library Program},
	booktitle = {Proceedings of the SIGCHI conference on Human factors in computing systems},
	series = {CHI {\textquoteright}97},
	year = {1997},
	month = {1997///},
	pages = {518 - 525},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {browse, design process, digital libraries, dynamic query, java, preview, search, web design},
	isbn = {0-89791-802-9},
	doi = {10.1145/258549.259009},
	url = {http://doi.acm.org/10.1145/258549.259009},
	author = {Plaisant, Catherine and Marchionini,Gary and Bruns,Tom and Komlodi,Anita and Campbell,Laura}
}
@conference {15577,
	title = {Algorithms for fast vector quantization},
	booktitle = {Data Compression Conference, 1993. DCC {\textquoteright}93.},
	year = {1993},
	month = {1993///},
	pages = {381 - 390},
	abstract = {This paper shows that if one is willing to relax the requirement of finding the true nearest neighbor, it is possible to achieve significant improvements in running time and at only a very small loss in the performance of the vector quantizer. The authors present three algorithms for nearest neighbor searching: standard and priority <e1>k </e1>-<e1>d</e1> tree search algorithms and a neighborhood graph search algorithm in which a directed graph is constructed for the point set and edges join neighboring points},
	keywords = {algorithm;, algorithms;, directed, fast, graph, graph;, graphs;, Neighborhood, performance;, problems;, quantisation;, quantization;, running, search, time;, tree, vector},
	doi = {10.1109/DCC.1993.253111},
	author = {Arya,S. and Mount, Dave}
}
