Gautam Das

George Washington University, Washington, Washington, D.C., United States

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Publications (153)33.81 Total impact

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    ABSTRACT: Location based services (LBS) have become very popular in recent years. They range from map services (e.g., Google Maps) that store geographic locations of points of interests, to online social networks (e.g., WeChat, Sina Weibo, FourSquare) that leverage user geographic locations to enable various recommendation functions. The public query interfaces of these services may be abstractly modeled as a kNN interface over a database of two dimensional points on a plane: given an arbitrary query point, the system returns the k points in the database that are nearest to the query point. In this paper we consider the problem of obtaining approximate estimates of SUM and COUNT aggregates by only querying such databases via their restrictive public interfaces. We distinguish between interfaces that return location information of the returned tuples (e.g., Google Maps), and interfaces that do not return location information (e.g., Sina Weibo). For both types of interfaces, we develop aggregate estimation algorithms that are based on novel techniques for precisely computing or approximately estimating the Voronoi cell of tuples. We discuss a comprehensive set of real-world experiments for testing our algorithms, including experiments on Google Maps, WeChat, and Sina Weibo.
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    Zhuojie Zhou, Nan Zhang, Gautam Das
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    ABSTRACT: How to enable efficient analytics over such data has been an increasingly important research problem. Given the sheer size of such social networks, many existing studies resort to sampling techniques that draw random nodes from an online social network through its restrictive web/API interface. Almost all of them use the exact same underlying technique of random walk - a Markov Chain Monte Carlo based method which iteratively transits from one node to its random neighbor. Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i.e., no access to the full graph topology). A problem with random walks, however, is the "burn-in" period which requires a large number of transitions/queries before the sampling distribution converges to a stationary value that enables the drawing of samples in a statistically valid manner. In this paper, we consider a novel problem of speeding up the fundamental design of random walks (i.e., reducing the number of queries it requires) without changing the stationary distribution it achieves - thereby enabling a more efficient "drop-in" replacement for existing sampling-based analytics techniques over online social networks. Our main idea is to leverage the history of random walks to construct a higher-ordered Markov chain. We develop two algorithms, Circulated Neighbors and Groupby Neighbors Random Walk (CNRW and GNRW) and prove that, no matter what the social network topology is, CNRW and GNRW offer better efficiency than baseline random walks while achieving the same stationary distribution. We demonstrate through extensive experiments on real-world social networks and synthetic graphs the superiority of our techniques over the existing ones.
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    ABSTRACT: In this work, we initiate the investigation of optimization opportunities in collaborative crowdsourcing. Many popular applications, such as collaborative document editing, sentence translation, or citizen science resort to this special form of human-based computing, where, crowd workers with appropriate skills and expertise are required to form groups to solve complex tasks. Central to any collaborative crowdsourcing process is the aspect of successful collaboration among the workers, which, for the first time, is formalized and then optimized in this work. Our formalism considers two main collaboration-related human factors, affinity and upper critical mass, appropriately adapted from organizational science and social theories. Our contributions are (a) proposing a comprehensive model for collaborative crowdsourcing optimization, (b) rigorous theoretical analyses to understand the hardness of the proposed problems, (c) an array of efficient exact and approximation algorithms with provable theoretical guarantees. Finally, we present a detailed set of experimental results stemming from two real-world collaborative crowdsourcing application us- ing Amazon Mechanical Turk, as well as conduct synthetic data analyses on scalability and qualitative aspects of our proposed algorithms. Our experimental results successfully demonstrate the efficacy of our proposed solutions.
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    ABSTRACT: Similarity search in large sequence databases is a problem ubiquitous in a wide range of application domains, including searching biological sequences. In this paper we focus on protein and DNA data, and we propose a novel approximate method method for speeding up range queries under the edit distance. Our method works in a filter-and-refine manner, and its key novelty is a query-sensitive mapping that transforms the original string space to a new string space of reduced dimensionality. Specifically, it first identifies the \(t\) most frequent codewords in the query, and then uses these codewords to convert both the query and the database to a more compact representation. This is achieved by replacing every occurrence of each codeword with a new letter and by removing the remaining parts of the strings. Using this new representation, our method identifies a set of candidate matches that are likely to satisfy the range query, and finally refines these candidates in the original space. The main advantage of our method, compared to alternative methods for whole sequence matching under the edit distance, is that it does not require any training to create the mapping, and it can handle large query lengths with negligible losses in accuracy. Our experimental evaluation demonstrates that, for higher range values and large query sizes, our method produces significantly lower costs and runtimes compared to two state-of-the-art competitor methods.
    Data Mining and Knowledge Discovery 01/2015; DOI:10.1007/s10618-015-0413-2 · 1.74 Impact Factor
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    ABSTRACT: The kNN query interface is a popular search interface for many real-world web databases. Given a user-specified query, the top-k nearest neighboring tuples (ranked by a predetermined ranking function) are returned. For example, many websites now provide social network features that recommend to a user others who share similar properties, interests, etc. Our studies of real-world websites unveil a novel yet serious privacy leakage caused by the design of such interfaces and ranking functions. Specifically, we find that many of such websites feature private attributes that are only visible to a user him/herself, but not to other users (and therefore will not be visible in the query answer). Nonetheless, these websites also take into account such private attributes in the design of the ranking function. While the conventional belief might be that tuple ranks alone are not enough to reveal the private attribute values, our investigation shows that this is not the case in reality. Specifically, we define a novel problem of rank based inference, and introduce a taxonomy of the problem space according to two dimensions, (1) the type of query interfaces widely used in practice and (2) the capability of adversaries. For each subspace of the prolem, we develop a novel technique which either guarantees the successful inference of private attributes, or (when such an inference is provably infeasible in the worst-case scenario) accomplishes such an inference attack for a significant portion of real-world tuples. We demonstrate the effectiveness and efficiency of our techniques through theoretical analysis and extensive experiments over real-world datasets, including successful online attacks over popular services such as Amazon Goodreads and Catch22dating.
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    ABSTRACT: Naeemul Hassan, Huadong Feng, Ramesh Venkataraman, Gautam Das, Chengkai Li, Nan Zhang
    CIKM; 11/2014
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    ABSTRACT: In this paper, we introduce a novel, general purpose, technique for faster sampling of nodes over an online social network. Specifically, unlike traditional random walk which wait for the convergence of sampling distribution to a predetermined target distribution - a waiting process that incurs a high query cost - we develop WALK-ESTIMATE, which starts with a much shorter random walk, and then proactively estimate the sampling probability for the node taken before using acceptance-rejection sampling to adjust the sampling probability to the predetermined target distribution. We present a novel backward random walk technique which provides provably unbiased estimations for the sampling probability, and demonstrate the superiority of WALK-ESTIMATE over traditional random walks through theoretical analysis and extensive experiments over real world online social networks.
  • Article: HDBTracker
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    ABSTRACT: We present IQR, a system that demonstrates optimization based interactive relaxations for queries that return an empty answer. Given an empty answer, IQR dynamically suggests one relaxation of the original query conditions at a time to the user, based on certain optimization objectives, and the user responds by either accepting or declining the relaxation, until the user arrives at a non-empty answer, or a non-empty answer is impossible to achieve with any further relaxations. The relaxation suggestions hinge on a proba- bilistic framework that takes into account the probability of the user accepting a suggested relaxation, as well as how much that relaxation serves towards the optimization objec- tive. IQR accepts a wide variety of optimization objectives - user centric objectives, such as, minimizing the number of user interactions (i.e., effort) or returning relevant results, as well as seller centric objectives, such as, maximizing profit. IQR offers principled exact and approximate solutions for gen- erating relaxations that are demonstrated using multiple, large real datasets.
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    ABSTRACT: Microblogging platforms such as Twitter have experienced a phenomenal growth of popularity in recent years, making them attractive platforms for research in diverse fields from computer science to sociology. However, most microblogging platforms impose strict access restrictions (e.g., API rate limits) that prevent scientists with limited resources - e.g., who cannot afford microblog-data-access subscriptions offered by GNIP et al. - to leverage the wealth of microblogs for analytics. For example, Twitter allows only 180 queries per 15 minutes, and its search API only returns tweets posted within the last week. In this paper, we consider a novel problem of estimating aggregate queries over microblogs, e.g., "how many users mentioned the word 'privacy' in 2013?". We propose novel solutions exploiting the user-timeline information that is publicly available in most microblogging platforms. Theoretical analysis and extensive real-world experiments over Twitter, Google+ and Tumblr confirm the effectiveness of our proposed techniques.
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    ABSTRACT: The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three components, user, item (e.g., photos in Flickr), and tags (i.e., words or phrases). Analyzing how tags are assigned by certain users to certain items has important implications in helping users search for desired information. In this paper, we develop a dual mining framework to explore tagging behavior. This framework is centered around two opposing measures, similarity and diversity, applied to one or more tagging components, and therefore enables a wide range of analysis scenarios such as characterizing similar users tagging diverse items with similar tags or diverse users tagging similar items with diverse tags. By adopting different concrete measures for similarity and diversity in the framework, we show that a wide range of concrete analysis problems can be defined and they are NP-Complete in general. We design four sets of efficient algorithms for solving many of those problems and demonstrate, through comprehensive experiments over real data, that our algorithms significantly out-perform the exact brute-force approach without compromising analysis result quality.
    The VLDB Journal 04/2014; 23(2). DOI:10.1007/s00778-013-0341-y · 1.70 Impact Factor
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    ABSTRACT: We formulate and investigate the novel problem of finding the skyline $k$-tuple groups from an $n$-tuple data set—i.e., groups of $k$ tuples which are not dominated by any other group of equal size, based on aggregate-based group dominance relationship. The major technical challenge is to identify effective anti-monotonic properties for pruning the search space of skyline groups. To this end, we first show that the anti-monotonic property in the well-known Apriori algorithm does not hold for skyline group pruning. Then, we identify two anti-monotonic properties with varying degrees of applicability: order-specific property which applies to SUM, MIN, and MAX as well as weak candidate-generation property which applies to MIN and MAX only. Experimental results on both real and synthetic data sets verify that the proposed algorithms achieve orders of magnitude performance gain over the baseline method.
    IEEE Transactions on Knowledge and Data Engineering 04/2014; 26(4):942-956. DOI:10.1109/TKDE.2013.119 · 1.82 Impact Factor
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    ABSTRACT: Many databases on the web are "hidden" behind (i.e., accessible only through) their restrictive, form-like, search interfaces. Recent studies have shown that it is possible to estimate aggregate query answers over such hidden web databases by issuing a small number of carefully designed search queries through the restrictive web interface. A problem with these existing work, however, is that they all assume the underlying database to be static, while most real-world web databases (e.g., Amazon, eBay) are frequently updated. In this paper, we study the novel problem of estimating/tracking aggregates over dynamic hidden web databases while adhering to the stringent query-cost limitation they enforce (e.g., at most 1,000 search queries per day). Theoretical analysis and extensive real-world experiments demonstrate the effectiveness of our proposed algorithms and their superiority over baseline solutions (e.g., the repeated execution of algorithms designed for static web databases).
    03/2014; 7(12). DOI:10.14778/2732977.2732985
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    ABSTRACT: We examine the problem of enabling the flexibility of updating one's preferences in group recommendation. In our setting, any group member can provide a vector of preferences that, in addition to past preferences and other group members' preferences, will be accounted for in computing group recommendation. This functionality is essential in many group recommendation applications, such as travel planning, online games, book clubs, or strategic voting, as it has been previously shown that user preferences may vary depending on mood, context, and company (i.e., other people in the group). Preferences are enforced in an feedback box that replaces preferences provided by the users by a potentially different feedback vector that is better suited for maximizing the individual satisfaction when computing the group recommendation. The feedback box interacts with a traditional recommendation box that implements a group consensus semantics in the form of Aggregated Voting or Least Misery, two popular aggregation functions for group recommendation. We develop efficient algorithms to compute robust group recommendations that are appropriate in situations where users have changing preferences. Our extensive empirical study on real world data-sets validates our findings.
    2014 IEEE 30th International Conference on Data Engineering (ICDE); 03/2014
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    ABSTRACT: We assume a database of items in which each item is described by a set of attributes, some of which could be multi-valued. We refer to each of the distinct attribute values as a feature. We also assume that we have information about the interactions (such as visits or likes) between a set of users and those items. In our paper, we would like to rank the features of an item using user-item interactions. For instance, if the items are movies, features could be actors, directors or genres, and user-item interaction could be user liking the movie. These information could be used to identify the most important actors for each movie. While users are drawn to an item due to a subset of its features, a user-item interaction only provides an expression of user preference over the entire item, and not its component features. We design algorithms to rank the features of an item depending on whether interaction information is available at aggregated or individual level granularity and extend them to rank composite features (set of features). Our algorithms are based on constrained least squares, network flow and non-trivial adaptations to non-negative matrix factorization. We evaluate our algorithms using both real-world and synthetic datasets.
    2014 IEEE 30th International Conference on Data Engineering (ICDE); 03/2014
  • Nan Zhang, Gautam Das
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    ABSTRACT: With the proliferation of very large data repositories hidden behind web interfaces, e.g., keyword search, form-like search and hierarchical/graph-based browsing interfaces for Amazon.com, eBay.com, etc., efficient ways of searching, exploring and/or mining such web data are of increasing importance. There are two key challenges facing these tasks: how to properly understand web interfaces, and how to bypass the interface restrictions. In this tutorial, we start with a general overview of web search and data mining, including various exciting applications enabled by the effective search, exploration, and mining of web repositories. Then, we focus on the fundamental developments in the field, including web interface understanding, crawling, sampling, and data analytics over web repositories with various types of interfaces. We also discuss the potential changes required for query processing, data mining and machine learning algorithms to be applied to web data. Our goal is two-fold: one is to promote the awareness of existing web data search/explora-tion/mining techniques among all web researchers who are interested in leveraging web data, and the other is to encourage researchers, especially those who have not previously worked in web search and mining before, to initiate their own research in these exciting areas.
    Proceedings of the 7th ACM international conference on Web search and data mining; 02/2014
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    ABSTRACT: We present SmartCrowd, a framework for optimizing collaborative knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by accounting for human factors in the process of assigning tasks to workers. Human factors designate workers' expertise in different skills, their expected minimum wage, and their availability. In SmartCrowd, we formulate task assignment as an optimization problem, and rely on pre-indexing workers and maintaining the indexes adaptively, in such a way that the task assignment process gets optimized both qualitatively, and computation time-wise. We present rigorous theoretical analyses of the optimization problem and propose optimal and approximation algorithms. We finally perform extensive performance and quality experiments using real and synthetic data to demonstrate that adaptive indexing in SmartCrowd is necessary to achieve efficient high quality task assignment.
    The VLDB Journal 01/2014; DOI:10.1007/s00778-015-0385-2 · 1.70 Impact Factor
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    ABSTRACT: The widespread use and growing popularity of online collaborative content sites has created rich resources for users to consult in order to make purchasing decisions on various items such as e-commerce products, restaurants, etc. Ideally, a user wants to quickly decide whether an item is desirable, from the list of items returned as a result of her search query. This has created new challenges for producers/manufacturers (e.g., Dell) or retailers (e.g., Amazon, eBay) of such items to compose succinct summarizations of web item descriptions, henceforth referred to as snippets, that are likely to maximize the items' visibility among users. We exploit the availability of user feedback in collaborative content sites in the form of tags to identify the most important item attributes that must be highlighted in an item snippet. We investigate the problem of finding the top-k best snippets for an item that are likely to maximize the probability that the user preference (available in the form of search query) is satisfied. Since a search query returns multiple relevant items, we also study the problem of finding the best diverse set of snippets for the items in order to maximize the probability of a user liking at least one of the top items. We develop an exact top-k algorithm for each of the problem and perform detailed experiments on synthetic and real data crawled from the web to to demonstrate the utility of our problems and effectiveness of our solutions.
    Proceedings of the 22nd ACM international conference on Conference on information & knowledge management; 10/2013

Publication Stats

4k Citations
33.81 Total Impact Points

Institutions

  • 2012–2014
    • George Washington University
      • Department of Computer Science
      Washington, Washington, D.C., United States
  • 2011–2014
    • Qatar Computing Research Institute
      Ad Dawḩah, Baladīyat ad Dawḩah, Qatar
  • 2–2014
    • University of Texas at Arlington
      • Department of Computer Sciences & Engineering
      Arlington, Texas, United States
  • 2008
    • University of Helsinki
      • Department of Computer Science
      Helsinki, Southern Finland Province, Finland
  • 2006
    • Banner College - Arlington
      Arlington, Texas, United States
  • 1993–2006
    • The University of Memphis
      • Department of Mathematical Sciences
      Memphis, TN, United States
  • 2005
    • National Technical University of Athens
      Athínai, Attica, Greece
  • 2000–2004
    • Microsoft
      Washington, West Virginia, United States