Xike Xie

Evaluating probability threshold k-nearest-neighbor queries over uncertain data

In emerging applications such as location-based services, sensor monitoring and biological management systems, the values of the database items are naturally imprecise. For these uncertain databases, an important query is the Probabilistic k-Nearest-Neighbor Query (k-PNN), which computes the probabilities of sets of k objects for being the closest to a given query point. The evaluation of this query can be both computationally- and I/O-expensive, since there is an exponentially large number of k object-sets, and numerical integration is required. Often a user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Probabilistic Threshold k-Nearest-Neighbor Query (T-k-PNN), which returns sets of k objects that satisfy the query with probabilities higher than some threshold T. Three steps are proposed to handle this query efficiently. In the first stage, objects that cannot constitute an answer are filtered with the aid of a spatial index. The second step, called probabilistic candidate selection, significantly prunes a number of candidate sets to be examined. The remaining sets are sent for verification, which derives the lower and upper bounds of answer probabilities, so that a candidate set can be quickly decided on whether it should be included in the answer. We also examine spatially-efficient data structures that support these methods. Our solution can be applied to uncertain data with arbitrary probability density functions. We have also performed extensive experiments to examine the effectiveness of our methods.

Survey of real-time processing systems for big data

In recent years, real-time processing and analytics systems for big data--in the context of Business Intelligence (BI)--have received a growing attention. The traditional BI platforms that perform regular updates on daily, weekly or monthly basis are no longer adequate to satisfy the fast-changing business environments. However, due to the nature of big data, it has become a challenge to achieve the real-time capability using the traditional technologies. The recent distributed computing technology, MapReduce, provides off-the-shelf high scalability that can significantly shorten the processing time for big data; Its open-source implementation such as Hadoop has become the de-facto standard for processing big data, however, Hadoop has the limitation of supporting real-time updates. The improvements in Hadoop for the real-time capability, and the other alternative real-time frameworks have been emerging in recent years. This paper presents a survey of the open source technologies that support big data processing in a real-time/near real-time fashion, including their system architectures and platforms.

UV-diagram: A Voronoi diagram for uncertain data

The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. In this paper, we study how the Voronoi diagram can be used on uncertain data, which are inherent in scientific and business applications. In particular, we propose the Uncertain-Voronoi Diagram (or UV-diagram in short). Conceptually, the data space is divided into distinct “UV-partitions”, where each UV-partition P is associated with a set S of objects; any point q located in P has the set S as its nearest neighbor with non-zero probabilities. The UV-diagram facilitates queries that inquire objects for having non-zero chances of being the nearest neighbor of a given query point. It also allows analysis of nearest neighbor information, e.g., finding out how many objects are the nearest neighbors in a given area. However, a UV-diagram requires exponential construction and storage costs. To tackle these problems, we devise an alternative representation for UV-partitions, and develop an adaptive index for the UV-diagram. This index can be constructed in polynomial time. We examine how it can be extended to support other related queries. We also perform extensive experiments to validate the effectiveness of our approach.

Finding traffic-aware fastest paths in spatial networks

Route planning and recommendation have received significant attention in recent years. In this light, we propose and investigate the novel problem of finding traffic-aware fastest paths (TAFP query) in spatial networks by considering the related traffic conditions. Given a sequence of user specified intended places Oq and a departure time t, TAFP finds the fastest path connecting Oq in order to guarantee that moving objects (e.g., travelers and bags) can arrive at the destination in time. This type of query is mainly motivated by indoor space applications, but is also applicable in outdoor space, and we believe that it may bring important benefits to users in many popular applications, such as tracking VIP bags in airports and recommending convenient routes to travelers. TAFP is challenged by two difficulties: (i) how to model the traffic awareness practically, and (ii) how to evaluate TAFP efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network Gta(V,E) by analysing uncertain trajectory data of moving objects. Based on Gta(V,E), two efficient algorithms are developed based on best-first and heuristic search strategies to evaluate TAFP query. The performance of TAFP has been verified by extensive experiments on real and synthetic spatial datasets.

Efficient distance-aware query evaluation on indoor moving objects

Indoor spaces accommodate large parts of peoples life. The increasing availability of indoor positioning, driven by technologies like Wi-Fi, RFID, and Bluetooth, enables a variety of indoor location-based services (LBSs). Efficient indoor distance-aware queries on indoor moving objects play an important role in supporting and boosting such LBSs. However, the distance-aware query evaluation on indoor moving objects is challenging because: (1) indoor spaces are characterized by many special entities and thus render distance calculation very complex; (2) the limitations of indoor positioning technologies create inherent uncertainties in indoor moving objects data. In this paper, we propose a complete set of techniques for efficient distance-aware queries on indoor moving objects. We define and categorize the indoor distances in relation to indoor uncertain objects, and derive different distance bounds that can facilitate query evaluation. Existing works often assume indoor floor plans are static, and require extensive pre-computation on indoor topologies. In contrast, we design a composite index scheme that integrates indoor geometries, indoor topologies, as well as indoor uncertain objects, and thus supports indoor distance-aware queries efficiently without time-consuming and volatile distance computation. We design algorithms for range query and k nearest neighbor query on indoor moving objects. The results of extensive experimental studies demonstrate that our proposals are efficient and scalable in evaluating distance-aware queries over indoor moving objects.

UV-diagram: a voronoi diagram for uncertain spatial databases

The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. We study how the Voronoi diagram can be used for uncertain spatial data, which are inherent in scientific and business applications. Specifically, we propose the Uncertain-Voronoi diagram (or UV-diagram), which divides the data space into disjoint “UV-partitions”. Each UV-partition

Modeling of Traffic-Aware Travel Time in Spatial Networks

P

Handling False Negatives in Indoor RFID Data

is associated with a set