Direction-based surrounder queries for mobile recommendations

Location-based recommendation services recommend objects to the user based on the user’s preferences. In general, the nearest objects are good choices considering their spatial proximity to the user. However, not only the distance of an object to the user but also their directional relationship are important. Motivated by these, we propose a new spatial query, namely a direction-based surrounder (DBS) query, which retrieves the nearest objects around the user from different directions. We define the DBS query not only in a two-dimensional Euclidean space \mathbbE{\mathbb{E}} but also in a road network \mathbbR{\mathbb{R}} . In the Euclidean space \mathbbE{\mathbb{E}} , we consider two objects a and b are directional close w.r.t. a query point q iff the included angle Daqb{\angle aqb} is bounded by a threshold specified by the user at the query time. In a road network \mathbbR{\mathbb{R}} , we consider two objects a and b are directional close iff their shortest paths to q overlap. We say object a dominates object b iff they are directional close and meanwhile a is closer to q than b. All the objects that are not dominated by others based on the above dominance relationship constitute direction-based surrounders (DBSs). In this paper, we formalize the DBS query, study it in both the snapshot and continuous settings, and conduct extensive experiments with both real and synthetic datasets to evaluate our proposed algorithms. The experimental results demonstrate that the proposed algorithms can answer DBS queries efficiently.     

A direction-based geographic routing scheme for intermittently connected mobile networks

In an intermittently connected mobile network, a complete routing path from a source to a destination cannot be guaranteed most of the time. Therefore, traditional routing methods for mobile ad hoc networks are not applicable in such a network. Current approaches for intermittently connected mobile networks are primarily based on redundant transmission and single-copy opportunistic routing. However, they incur either high overhead due to excessive transmissions, or long delay due to incorrect path choices during forwarding. In this paper, we propose a direction-based geographic (DIG) routing scheme for intermittently connected mobile networks. Relying on geographic location information, the packets are routed in a path approximately to the shortest path from the source node to the destination, which significantly reduces the overhead in redundant transmission and decreases the transmission delay in the single-copy opportunistic routing. Theoretical analysis and trace-driven experimental results show that DIG provides low transmission delay with low overhead in comparison with the schemes in the redundant transmission and single-copy opportunistic routing.     

A time cost optimization for similar scenarios mobile GIS queries

Query optimization strategies have been recently employed for various types of queries such as Geographic Information Systems (GIS) in particular Location Based Services and proximity analysis. This work proposes a new decision making mechanism called Time Cost Optimizer (TCOP) which aims to effectively reduce the cost of execution for multiple mobile GIS queries. TCOP is implemented in order to employ the new paradigm Sharing Global Execution Plans (GEP) where multiple users share the previously melted templates. It is integrated as a new component of the Query Melting Processor (QMP) that has been developed for processing multiple dynamic complex queries based on the Query Melting (QM) paradigm which consists of the sharing paradigm and the push-down strategy. An experimental evaluation has been conducted using a case study based on the map of Paris. The results analysis proved that significant saving in time can be achieved by employing the newly developed strategy.     

Pull-based recommendations in mobile environments

In the field of Context-Aware Recommendation Systems (CARS), only static contextual information is usually considered. However, the dynamic contextual information would very helpful in mobile computing scenarios. Despite this interest, the design and implementation of flexible and generic frameworks to support an easy development of context-aware mobile recommendation systems have been relatively unexplored. In this paper, we describe a framework that facilitates the development of CARS for mobile environments. We mainly focus on the development of the elements needed to support pull-based recommendations and the experimental evaluation of the proposed system.     

Dependent data broadcasting for unordered queries in a multiple channel mobile environment

Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment, and as a consequence, the results are of limited applicability to the upcoming mobile environments. In view of this, we relax this restriction and explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to broadcast programs of very high quality.     

A general methodology for direction-based irregular routing algorithms

This paper presents a general methodology for generating deadlock-free routing algorithms for irregular networks. Constructing a spanning tree on the given network, assigning directions to the network channels, creating deadlock-free zones, and specifying a logical sequence of the produced deadlock-free zones are the four fundamental steps that the proposed methodology takes to generate deadlock-free and connected routing algorithms. By applying the proposed methodology with two known labeling methods we have generated six irregular routing algorithms: three of them are novel routing algorithms and three of them (the Up/Down, Left/Right, and L-turn routing algorithms) have already been proposed in the literature. Extensive simulation experiments have been performed considering various network topologies, different network sizes (considering different network nodes and network channels), various message lengths, a variety of spanning tree roots, and a wide range of message (traffic) generation rates. Simulation results show that the six routing algorithms can be divided into three pairs. Routing members of each pair show similar behavior in terms of message latencies and saturation generation rates. However, it is worth noting that for a given topology the performance of the six routing algorithms may be totally different and it mainly depends on the network topology.     

Supporting multi-dimensional queries in mobile P2P network

Nowadays, as the mobile services become widely used, there is a strong demand for mobile support in P2P search techniques. In this paper, we introduce a new cost model for searching multi-dimensional data in mobile P2P environment and propose a novel multi-dimensional mobile P2P search framework called MIME. MIME models the physical node layout in a two-dimensional plane and keeps records of the locations of the nodes to construct a proximity-aware P2P overlay. MIME is able to employ two different split schemes for the construction of the overlay. We propose query processing techniques for such P2P overlay. In addition, we employ a novel expanding method for tuning the performance of KNN queries in MIME. We also discuss two adaptive features incorporated into MIME to support mobility: an update algorithm that makes dynamic updates to the overlay, and a cache mechanism that reduces the load of data migration during the updates. The experimental results show that the proposed techniques are effective, and that MIME achieves significant performance improvements in Point, Range, and KNN queries compared to the conventional system.     

VGQ-Vor: extending virtual grid quadtree with Voronoi diagram for mobile k nearest neighbor queries over mobile objects

Performing mobile k nearest neighbor (MkNN) queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried aremobile. The performance of this kind of query relies heavily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient update operations and efficient query handling. This study aims to improve the performance of the MkNN queries while reducing update costs. Our approach is based on an observation that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile objects. We first propose an virtual grid quadtree with Voronoi diagram(VGQ-Vor), which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor (kNN) query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The experimental results show that the VGQ-Vor outperforms the existing techniques (Bx-tree, Bdual-tree) by one to three orders of magnitude in most cases.     

A cache invalidation scheme for continuous partial match queries in mobile computing environments

The continuous partial match query is a partial match query whose result remains consistently in the client’s memory. Conventional cache invalidation methods for mobile clients are record ID-based. However, since the partial match query uses content-based retrieval, the conventional ID-based approaches cannot efficiently manage the cache consistency of mobile clients. In this paper, we propose a predicate-based cache invalidation scheme for continuous partial match queries in mobile computing environments. We represent the cache state of a mobile client as a predicate, and also construct a cache invalidation report (CIR), which the server broadcasts to clients for cache management, with predicates. In order to reduce the amount of information that is needed for cache management, we propose a set of methods for CIR construction (in the server) and identification of invalidated data (in the client). Through experiments, we show that the predicate-based approach is very effective for the cache management of mobile clients.     

Nearest neighbor queries with peer-to-peer data sharing in mobile environments

Nearest neighbor queries, such as determining the proximity of stationary objects (e.g., restaurants and gas stations) are an important class of inquiries for supporting location-based services. We present a novel approach to support nearest neighbor queries from mobile hosts by leveraging the sharing capabilities of wireless ad-hoc networks. We illustrate how previous query results cached in the local storage of neighboring mobile users can be leveraged to either fully or partially compute and verify nearest neighbor queries at a local host. The feasibility and appeal of our technique is illustrated through extensive simulation results that indicate a considerable reduction of the query load on the remote database. Furthermore, the scalability of our approach is excellent because a higher density of mobile hosts increases its effectiveness.     

Enhancing mobile Web access using intelligent recommendations

Intelligent Web recommendations derived from frequent user Web-access patterns can help typical mobile users efficiently navigate standard Web sites. To facilitate mobile Web access, mobile clients' display resolution must gradually increase. Additionally, servers need a simple and elegant solution for displaying content effectively on mobile devices. We propose an implicit server-side approach using intelligent Web recommendations that can significantly enhance the mobile-browsing experience.     

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.     

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment.     

DMEA-II: the direction-based multi-objective evolutionary algorithm-II

This paper discusses the use of direction of improvement in guiding multi-objective evolutionary algorithms (MOEAs) during the search process towards the area of Pareto optimal set. We particularly propose a new version of the Direction based Multi-objective Evolutionary Algorithm (DMEA) and name it as DMEA-II. The new features of DMEA-II includes (1) an adaptation of the balance between convergence and spreading by using an adaptive ratio between the convergence and spreading directions being selected over time; (2) a new concept of ray-based density for niching; and (3) a new selection scheme based on the ray-based density for selecting solutions for the next generation. To validate the performance of DMEA-II, we carried out a case study on a wide range of test problems and comparison with other MOEAs. It obtained quite good results on primary performance metrics, namely the generation distance, inverse generation distance, hypervolume and the coverage set. Our analysis on the results indicates the better performance of DMEA-II in comparison with the most popular MOEAs.     

Correcting queries for XML

It has been observed that queries over XML data sources are often unsatisfiable. Unsatisfiability may stem from several different sources, e.g., the user may be insufficiently familiar with the labels appearing the documents, or may not be intimately aware of the hierarchical structure of the documents. To deal with query and document mismatches, previous research has considered returning answers that maximally satisfy (in some sense) the query, instead of only returning strictly satisfying answers. However, this breaks the golden database rule that only strictly satisfying answers are returned when querying. Indeed, the relationship between the query and answers is no longer clear, when unsatisfying answers are returned. To reinstate the golden database rule, this article proposes a framework for automatically correcting queries over XML. This framework generates similar satisfiable queries, when the user query is unsatisfiable. The user can then choose a satisfiable query of interest, and receive exactly satisfying answers to this query.     

Explaining similarity for SPARQL queries

World Wide Web - Knowledge graph has gained significant popularity in recent years. As one of the W3C standards, SPARQL has become the de facto standard query language to retrieve the desired data...     

Sampling-based estimators for subset-based queries

We consider the problem of using sampling to estimate the result of an aggregation operation over a subset-based SQL query, where a subquery is correlated to an outer query by a NOT EXISTS, NOT IN, EXISTS or IN clause. We design an unbiased estimator for our query and prove that it is indeed unbiased. We then provide a second, biased estimator that makes use of the superpopulation concept from statistics to minimize the mean squared error of the resulting estimate. The two estimators are tested over an extensive set of experiments. Material in this paper is based upon work supported by the National Science Foundation via grants 0347408 and 0612170.     

Context-aware recommendations through context and activity recognition in a mobile environment

The mobile Internet introduces new opportunities to gain insight in the user’s environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user’s current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.