Search

Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter $d_c$ d c , which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index. [ABSTRACT FROM AUTHOR]

Destination prediction is known as an important problem for many location based services (LBSs). Existing solutions generally apply probabilistic models or neural network models to predict destinations over a subtrajectory, and adopt the standard attention mechanism to improve the prediction accuracy. However, the standard attention mechanism uses fixed feature representations, and has a limited ability to represent distinct features of locations. Besides, existing methods rarely take the impact of spatial and temporal characteristics of the trajectory into account. Their accuracies in fine-granularity prediction are always not satisfactory due to the data sparsity problem. Thus, in this paper, a carefully designed deep learning model called LATL model is presented. It not only adopts an adaptive attention network to model the distinct features of locations, but also implements time gates and distance gates into the Long Short-Term Memory (LSTM) network to capture the spatial-temporal relation between consecutive locations. Furthermore, to better understand the mobility patterns in different spatial granularities, and explore the fusion of multi-granularity learning capability, a hierarchical model that utilizes tailored combination of different neural networks under multiple spatial granularities is further proposed. Extensive empirical studies verify that the newly proposed models perform effectively and settle the problem nicely. [ABSTRACT FROM AUTHOR]

A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency. [ABSTRACT FROM AUTHOR]

Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising. [ABSTRACT FROM AUTHOR]

The urban road networks undergo frequent traffic congestions during the peak hours and around the city center. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion scenario is often represented by a set of distinguishable network partitions that have a homogeneous level of congestion inside them but are heterogeneous to others. Due to the dynamic nature of traffic, these partitions evolve with time in terms of their structure and location. In this paper, we propose a comprehensive framework to capture the evolution by incrementally updating the partitions in an efficient manner using a two-layer approach. The physical layer maintains a set of small-sized road network building blocks in a fine granularity, and performs low-level computations to incrementally update them, whereas the logical layer performs high-level computations in order to serve as an interface to query the physical layer about the congested partitions in a coarse granularity. We also propose an in-memory index called Bin that compactly stores the historical sets of building blocks in the main memory with no information loss, and facilitates their efficient retrieval. Our experimental results show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy. The proposed Bin consumes a minimum space with least redundancy at different time stamps. [ABSTRACT FROM AUTHOR]

Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road networks, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains a large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that the binary code can preserve distance information, which is very helpful for LBS applications. In addition, an unresolved question in this area is whether it is possible to perform a search directly on the compressed trajectories, and if the answer is yes, then how. Here, we show that directly querying compressed trajectories based on our encoding scheme is possible and can be done efficiently. We design a set of primitive operations for this purpose, and propose index structures to reduce query response time. We demonstrate the advantage of our method and compare it against existing ones through a thorough experimental study on real trajectories in road network. [ABSTRACT FROM AUTHOR]

Smart cities have been recognized as a promising research focus around the world. To realize smart cities, computation and utilization of big data are key factors. More specifically, exploring the patterns of human mobility based on large amounts of multi-source data plays an important role in analyzing the formation of social-economic phenomena in smart cities. However, our acquired knowledge is still very limited for smart cities. In this article, we propose an integrated computing method to rescale heterogeneous traffic trajectory data, which leverages MLE and BIC. Our analysis is based on two real datasets generated by subway smart card transactions and taxi GPS trajectories from Shanghai, China, which contain more than 451 million trading records by 14 subway lines and 34 billion GPS records by 13,695 taxis. Specifically, we quantitatively explore the patterns of human mobility on weekends and weekdays. Through logarithmic binning and data fitness, we calculate the Bayesian weights to select the best fitting distributions. In addition, we leverage three metrics to analyze the patterns of human mobility in two datasets: trip displacement, trip duration, and trip interval. We obtain several important human mobility patterns and discover quite a few interesting phenomena, which lay a solid foundation for future research. [ABSTRACT FROM AUTHOR]

String similarity join, as an essential operation in applications including data integration and data cleaning, has attracted significant attention in the research community. Previous studies focus on global similarity join. In this paper, we study local similarity join with edit distance constraints, which finds string pairs from two string collections that have similar substrings. We study two kinds of local similarity join problems: checking local similar pairs and locating local similar pairs. We first consider the case where if two strings are locally similar to each other, they must share a common gram of a certain length. We show how to do efficient local similarity verification based on a matching gram pair. We propose two pruning techniques and an incremental method to further improve the efficiency of finding matching gram pairs. Then, we devise a method to locate the longest similar substring pair for two local similar strings. We conducted a comprehensive experimental study to evaluate the efficiency of these techniques. [ABSTRACT FROM PUBLISHER]

Influence maximization is a recent but well-studied problem which helps identify a small set of users that are most likely to “influence” the maximum number of users in a social network. The problem has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the problem of influence maximization in location-aware social networks, or, more generally, Geo-social Influence Spanning Maximization. Given a query $q$ composed of a region $R$ , a regional acceptance rate $\rho$ , and an integer $k$ as a seed selection budget, our aim is to find the maximum geographic spanning regions (MGSR). We refer to this as the MGSR problem. Our approach differs from previous work as we focus more on identifying the maximum spanning geographical regions within a region $R$ , rather than just the number of activated users in the given network like the traditional influence maximization problem  [14] . Our research approach can be effectively used for online marketing campaigns that depend on the physical location of social users. To address the MGSR problem, we first prove NP-Hardness. Next, we present a greedy algorithm with a $1-1/e$ approximation ratio to solve the problem, and further improve the efficiency by developing an upper bounded pruning approach. Then, we propose the OIR -Tree index, which is a hybrid index combining ordered influential node lists with an R -tree. We show that our index based approach is significantly more efficient than the greedy algorithm and the upper bounded pruning algorithm, especially when $k$ is large. Finally, we evaluate the performance for all of the proposed approaches using three real datasets. [ABSTRACT FROM AUTHOR]

Recent studies show that individuals in a social network can be divided into different groups of densely connected communities, and these individuals who bridge different communities, referred to as structural hole spanners, have great potential to acquire resources/information from communities and thus benefit from the access. Structural hole spanners are crucial in many real applications such as community detections, diffusion controls, viral marketing, etc. In spite of their importance, little attention has been paid to them. Particularly, how to accurately characterize the structural hole spanners and how to devise efficient yet scalable algorithms to find them in a large social network are fundamental issues. In this paper, we study the top-$k$ structural hole spanner problem. We first provide a novel model to measure the quality of structural hole spanners through exploiting the structural hole spanner properties. Due to its NP-hardness, we then devise two efficient yet scalable algorithms, by developing innovative filtering techniques that can filter out unlikely solutions as quickly as possible, while the proposed techniques are built up on fast estimations of the upper and lower bounds on the cost of an optimal solution and make use of articulation points in real social networks. We finally conduct extensive experiments to validate the effectiveness of the proposed model, and to evaluate the performance of the proposed algorithms using real world datasets. The experimental results demonstrate that the proposed model can capture the characteristics of structural hole spanners accurately, and the structural hole spanners found by the proposed algorithms are much better than those by existing algorithms in all considered social networks, while the running times of the proposed algorithms are very fast. [ABSTRACT FROM AUTHOR]

Social networks are a vital mechanism to disseminate information to friends and colleagues. In this work, we investigate an important problem—the personalized influential topic search, or PIT-Search in a social network: Given a keyword query $q$ issued by a user $u$ in a social network, a PIT-Search is to find the top-$k$ $q$-related topics that are most influential for the query user $u$. The influence of a topic to a query user depends on the social connection between the query user and the social users containing the topic in the social network. To measure the topics’ influence at the similar granularity scale, we need to extract the social summarization of the social network regarding topics. To make effective topic-aware social summarization, we propose two random-walk based approaches: random clustering and an L-length random walk. Based on the proposed approaches, we can find a small set of representative users with assigned influential scores to simulate the influence of the large number of topic users in the social network with regards to the topic. The selected representative users are denoted as the social summarization of topic-aware influence spread over the social network. And then, we verify the usefulness of the social summarization by applying it to the problem of personalized influential topic search. Finally, we evaluate the performance of our algorithms using real-world datasets, and show the approach is efficient and effective in practice. [ABSTRACT FROM PUBLISHER]

Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph $q$ and a set of missing ( why-not) graphs, we aim to modify $q$ into a new query graph $q^*$ such that the missing graphs are included in the new answer set of $q^*$ . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for $q^*$ . Then, we present a two-phase algorithm to find the new query $q^*$ . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query $q$ within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for $q^*$ . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets. [ABSTRACT FROM PUBLISHER]

While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we first derive keyword search candidates of the query by a simple feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, two efficient algorithms are proposed to incrementally compute top-$k$ qualified query candidates as the diversified search intentions. Two selection criteria are targeted: the $k$ selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results. At last, a comprehensive evaluation on real and synthetic data sets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms. [ABSTRACT FROM PUBLISHER]

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ)over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI)index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches. [ABSTRACT FROM AUTHOR]

Functional and inclusion dependency discovery is important to knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional dependency, approximate functional dependency, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies. [ABSTRACT FROM PUBLISHER]