Dimitrios Papadias

Location-based spatial queries

In this paper we propose an approach that enables mobile clients to determine the validity of previous queries based on their current locations. In order to make this possible, the server returns in addition to the query result, a validity region around the clients location within which the result remains the same. We focus on two of the most common spatial query types, namely nearest neighbor and window queries, define the validity region in each case and propose the corresponding query processing algorithms. In addition, we provide analytical models for estimating the expected size of the validity region. Our techniques can significantly reduce the number of queries issued to the server, while introducing minimal computational and network overhead compared to traditional spatial queries.

Assessing multimedia similarity: a framework for structure and motion

In this paper we address the issue of structural multimedia similarity, which is based on the relations between the individual objects that comprise a multimedia document. We propose a binary string encoding for 1D relations which permits the automatic derivation of similarity measures. We then extend it to various resolution levels and many dimensions and show that reasoning on spatiotemporal structure is significantly facilitated in the new framework, by applying it to multimedia presentation and motion similarity.

Geo-Social Ranking: functions and query processing

Given a query location q, Geo-Social Ranking (GSR) ranks the users of a Geo-Social Network based on their distance to q, the number of their friends in the vicinity of q, and possibly the connectivity of those friends. We propose a general GSR framework and four GSR functions that assign scores in different ways: (i) LC, which is a weighted linear combination of social (i.e., friendships) and spatial (i.e., distance to q) aspects, (ii) RC, which is a ratio combination of the two aspects, (iii) HGS, which considers the number of friends in coincident circles centered at q, and (iv) GST, which takes into account triangles of friends in the vicinity of q. We investigate the behavior of the functions, qualitatively assess their results, and study the effects of their parameters. Moreover, for each ranking function, we design a query processing technique that utilizes its specific characteristics to efficiently retrieve the top-k users. Finally, we experimentally evaluate the performance of the top-k algorithms with real and synthetic datasets.

Uncertain Graph Processing through Representative Instances

Data in several applications can be represented as an uncertain graph whose edges are labeled with a probability of existence. Exact query processing on uncertain graphs is prohibitive for most applications, as it involves evaluation over an exponential number of instantiations. Thus, typical approaches employ Monte-Carlo sampling, which (i) draws a number of possible graphs (samples), (ii) evaluates the query on each of them, and (iii) aggregates the individual answers to generate the final result. However, this approach can also be extremely time consuming for large uncertain graphs commonly found in practice. To facilitate efficiency, we study the problem of extracting a single representative instance from an uncertain graph. Conventional processing techniques can then be applied on this representative to closely approximate the result on the original graph. In order to maintain data utility, the representative instance should preserve structural characteristics of the uncertain graph. We start with representatives that capture the expected vertex degrees, as this is a fundamental property of the graph topology. We then generalize the notion of vertex degree to the concept of n-clique cardinality, that is, the number of cliques of size n that contain a vertex. For the first problem, we propose two methods: Average Degree Rewiring (ADR), which is based on random edge rewiring, and Approximate B-Matching (ABM), which applies graph matching techniques. For the second problem, we develop a greedy approach and a game-theoretic framework. We experimentally demonstrate, with real uncertain graphs, that indeed the representative instances can be used to answer, efficiently and accurately, queries based on several metrics such as shortest path distance, clustering coefficient, and betweenness centrality.

Geo-social keyword search

In this paper, we propose Geo-Social Keyword (GSK) search, which enables the retrieval of users, points of interest (POIs), or keywords that satisfy geographic, social, and/or textual criteria. We first introduce a general GSK framework that covers a wide range of real-world tasks, including advertisement, context-based search, and market analysis. Then, we present three concrete GSK queries: (i) NPRU that returns the top-k users based on their spatial proximity to a given query location, their popularity, and their similarity to an input set of terms; (ii) NSTP that outputs the top-k POIs based on their proximity to a user v, the number of check-ins by friends of v, and their similarity to a set of terms; (iii) FSKR that discovers the top-k keywords based on their frequency in pairs of friends located within a spatial area. For each query, we develop a processing algorithm that utilizes a novel hybrid index. Finally, we evaluate our framework with thorough experiments using real datasets.

Modeling Negotiations in Group Decision Support Systems

Group decision making processes are usually characterized by multiple goals and conflicting arguments, brought up by decision makers with different backgrounds and interests. This paper describes a computational model of negotiation and argumentation, by which participants can express their claims and judgements, aiming at informing or convincing. The model is able to handle inconsistent, qualitative and incomplete information in cases where one has to weigh multiple criteria for and against the selection of a certain course of action. It is implemented in Java, the aim being to deploy it on the World Wide Web. The basic objects in our terminology are positions, issues, arguments pro and con, and preference relations. The paper describes procedures for consistency checking, preference aggregation and conclusion of issues under discussion. The proposed model combines concepts from various well-established areas, such as Multiple Criteria Decision Making, nonmonotonic reasoning and cognitive science.

Location-Based sponsored search advertising

The proliferation of powerful mobile devices with built-in navigational capabilities and the adoption in most metropolitan areas of fast wireless communication protocols have recently created unprecedented opportunities for location-based advertising. In this work, we provide models and investigate the market for location-based sponsored search, where advertisers pay the search engine to be displayed in slots alongside the search engines main results. We distinguish between three cases: (1) advertisers only declare bids but not budgets, (2) advertisers declare budgets but not bids, and (3) advertisers declare both bids and budgets. We first cast these problems as game theoretical market problems, and we subsequently attempt to identify the equilibrium strategies for the corresponding games.