Farnoush Banaei-Kashani

Voronoi-Based Geospatial Query Processing with MapReduce

Geospatial queries (GQ) have been used in a wide variety of applications such as decision support systems, profile-based marketing, bioinformatics and GIS. Most of the existing query-answering approaches assume centralized processing on a single machine although GQs are intrinsically parallelizable. There are some approaches that have been designed for parallel databases and cluster systems, however, these only apply to the systems with limited parallel processing capability, far from that of the cloud-based platforms. In this paper, we study the problem of parallel geos patial query processing with the MapReduce programming model. Our proposed approach creates a spatial index, Voronoi diagram, for given data points in 2D space and enables efficient processing of a wide range of GQs. We evaluated the performance of our proposed techniques and correspondingly compared them with their closest related work while varying the number of employed nodes.

SWAM: a family of access methods for similarity-search in peer-to-peer data networks

Peer-to-peer Data Networks (PDNs) are large-scale, self-organizing, distributed query processing systems. Familiar examples of PDN are peer-to-peer file-sharing networks, which support exact-match search queries to locate user-requested files. In this paper, we formalize the more general problem of <i>similarity-search</i> in PDNs, and propose a <i>family</i> of distributed access methods, termed <i>Small-World Access Methods (SWAM)</i>, for efficient execution of various similarity-search queries, namely exact-match, range, and k-nearest-neighbor queries. Unlike its predecessors, i.e., LH* and DHTs, SWAM does not control the assignment of data objects to PDN nodes; each node autonomously stores its own data. Besides, SWAM supports all similarity-search queries on multiple attributes. SWAM guarantees that the query object will be found (if it exists in the network) in average time logarithmically proportional to the network size. Moreover, once the query object is found, all the similar objects would be in its proximate network neighborhood and hence enabling efficient range and k-nearest-neighbor queries. As a specific instance of SWAM, we propose <i>SWAM-V</i>, a Voronoi-based SWAM that indexes PDNs with multi-attribute data objects. For a PDN with <i>N</i> nodes SWAM-V has query time, communication cost, and computation cost of <i>O</i>(log <i>N</i>) for exact-match queries, and <i>O</i>(log <i>N</i> + <b>s</b><i>N</i>) and <i>O</i>(log <i>N</i> + <b>k</b>) for range queries (with selectivity <b>s</b>) and <b>k</b>NN queries, respectively. Our experiments show that SWAM-V consistently outperforms a similarity-search enabled version of CAN in query time and communication cost by a factor of 2 to 3.

Online computation of fastest path in time-dependent spatial networks

The problem of point-to-point fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that travel-times of the network edges are constant. However, with real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. In this paper, we study the online computation of fastest path in time-dependent spatial networks and present a technique which speeds-up the path computation. We show that our fastest path computation based on a bidirectional time-dependent A* search significantly improves the computation time and storage complexity. With extensive experiments using real data-sets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.

Efficient k-nearest neighbor search in time-dependent spatial networks

The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., travel-time) of each edge in the spatial network is constant. However, in real-world, edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. In this paper, we study the problem of kNN search in time-dependent spatial networks where the weight of each edge is a function of time. We propose two novel indexing schemes, namely Tight Network Index (TNI) and Loose Network Index (LNI) to minimize the number of candidate nearest neighbor objects and, hence, reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.

Decentralized resource management for a distributed continuous media server

Distributed continuous media server (DCMS) architectures are proposed to minimize the communication-storage cost for those continuous media applications that serve a large number of geographically distributed clients. Typically, a DCMS is designed as a pure hierarchy of centralized continuous media servers. RedHi, a Redundant Hierarchical topology for DCMS networks, can result in higher utilization and better reliability over pure hierarchy. We focus on the design of a resource management system (RMS) for RedHi that can exploit the resources of its DCMS network to achieve these performance objectives. Our RMS is based on a fully decentralized approach to achieve optimal scalability and robustness. The major drawback of a fully decentralized design is the increase in latency time and communication overhead to locate the requested object. However, as compared to the typically long duration and high resource/bandwidth requirements of continuous media objects, the extra latency and overhead of a decentralized resource management approach become negligible. Moreover, our RMS collapses three management tasks (object location, path selection and resource reservation) into one fully decentralized object delivery mechanism, reducing the latency even further. Decentralization of the resource management satisfies our scalability and robustness objectives, whereas collapsing the management tasks helps alleviate the latency and overhead constraints. To achieve a high resource utilization, the object delivery scheme uses our proposed cost function, as well as various object location and resource reservation policies to select and allocate the best streaming path to serve each request. The object delivery scheme is designed as an application-layer resource management middleware for the DCMS architecture to be independent of the underlying telecommunication infrastructure. Experiments show that our RMS is successful in realization of the higher resource utilization for the DCMS networks with the RedHi topology.

A case for time-dependent shortest path computation in spatial networks

The problem of point-to-point shortest path computation in spatial networks is extensively studied with many approaches proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that weights (e.g., travel-time) of the network edges are constant. However, with real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time of the edge. With this paper, we study the applicability of existing shortest path algorithms to real-world large time-dependent spatial networks. In addition, we evaluate the importance of considering time-dependent edge travel-times for route planning in spatial networks. We show that time-dependent shortest path computation can reduce the travel-time by 36% on average as compared to the static shortest path computation that assumes constant edge travel-times.

Efficient Continuous Nearest Neighbor Query in Spatial Networks Using Euclidean Restriction

In this paper, we propose an efficient method to answer continuous k nearest neighbor (Ck NN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, Ck NN continuously monitors the nearest (in network distance) neighboring objects to the query. Previous Ck NN methods are inefficient and, hence, fail to scale in large networks with numerous data objects because: 1) they heavily rely on Dijkstra-based blind expansion for network distance computation that incurs excessively redundant cost particularly in large networks, and 2) they blindly map all object location updates to the network disregarding whether the updates are relevant to the Ck NN query result. With our method, termed ER-Ck NN (short for Euclidian Restriction based Ck NN), we utilize ER to address both of these shortcomings. Specifically, with ER we enable 1) guided search (rather than blind expansion) for efficient network distance calculation, and 2) localized mapping (rather than blind mapping) to avoid the intolerable cost of redundant object location mapping. We demonstrate the efficiency of ER-Ck NN via extensive experimental evaluations with real world datasets consisting of a variety of large spatial networks with numerous moving objects.

Towards modeling the traffic data on road networks

A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding time-dependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for location-based services) require realistic models that accurately represent the temporal behavior of such networks. In this paper, for the first time we propose a traffic modeling framework for road networks that enables 1) generating an accurate temporal model from archived temporal data collected from a spatiotemporal network (so as to be able to publish the temporal model of the spatiotemporal network without having to release the real data), and 2) augmenting any given spatial network model with a corresponding realistic temporal model custom-built for that specific spatial network (in order to be able to generate a spatiotemporal network model from a solely spatial network model). We validate the accuracy of our proposed modeling framework via experiments. We also used the proposed framework to generate the temporal model of the Los Angeles County freeway network and publish it for public use.

TransDec:A spatiotemporal query processing framework for transportation systems

In this paper, we present TransDec, an end-to-end-data-driven system which enables spatiotemporal queries in transportation systems with dynamic, real-time and historical data. TransDec fuses a variety of real-world spatiotemporal datasets including massive traffic sensor data, trajectory data, transportation network data, and point-of-interest data to create an immersive and realistic virtual model of a transportation system. With TransDec, we address the challenges in visualization, monitoring, querying and analysis of dynamic and large-scale transportation data in both time and space.

Towards k-nearest neighbor search in time-dependent spatial network databases

The class of k Nearest Neighbor (kNN) queries in spatial networks is extensively studied in the context of numerous applications. In this paper, for the first time we study a generalized form of this problem, called the Time-Dependent k Nearest Neighbor problem (TD-kNN) with which edge-weights are time variable. All existing approaches for kNN search assume that the weight (e.g., travel-time) of each edge of the spatial network is constant. However, in real-world edge-weights are time-dependent (i.e., the arrival-time to an edge determines the actual travel-time on that edge) and vary significantly in short durations. We study the applicability of two baseline solutions for TD-kNN and compare their efficiency via extensive experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data recordings.