DSI: A Fully Distributed Spatial Index for Location-Based Wireless Broadcast Services
Recent announcement of the MSN Direct Service has demonstrated the feasibility and industrial interest in utilizing wireless broadcast for pervasive information services. To support location-based services in wireless data broadcast systems, a distributed spatial index (called DSI) is proposed in this paper. DSI is highly efficient because it has a linear yet fully distributed structure that facilitates multiple search paths to be naturally mixed together by sharing links. Moreover, DSI is very resilient in error-prone wireless communication environments. Search algorithms for two classical location-based queries, window queries and kNN queries, based on DSI are presented. Performance evaluation of DSI shows that DSI significantly outperforms R-tree and Hilbert Curve Index, two state-of-the-art spatial indexing techniques for wireless data broadcast

title: The D-tree: an index structure for planar point queries in location-based wireless services
abstract: Location-based services (LBSs), considered as a killer application in the wireless data market, provide information based on locations specified in the queries. In this paper, we examine the indexing issue for querying location-dependent data in wireless LBSs; in particular, we focus on an important class of queries, planar point queries. To address the issues of responsiveness, energy consumption, and bandwidth contention in wireless communications, an index has to minimize the search time and maintain a small storage overhead. It is shown that the traditional point-location algorithms and spatial index structures fail to achieve either objective or both. This paper proposes a new index structure, called D-tree, which indexes spatial regions based on the divisions that form the boundaries of the regions. We describe how to construct a binary D-tree index, how to process queries based on the D-tree, and how to page the binary D-tree. Moreover, two parameterized methods for partitioning the original space, called fixed grid assignment (FGA) and adaptive grid assignment (AGA), are proposed to enhance the D-tree. The performance of the D-tree is evaluated using both synthetic and real data sets. Experimental results show that the proposed D-tree outperforms the well-known indexes such as the R/sup */-tree, and that both the FGA and AGA approaches can achieve different performance trade-offs between the index search time and storage overhead by fine-tuning their algorithmic parameters.

title: Caching Complementary Space for Location-Based Services
abstract: In this paper, we propose a novel client-side, multi-granularity caching scheme, called “Complementary Space Caching” (CS caching), for location-based services in mobile environments. Different from conventional data caching schemes that only cache a portion of dataset, CS caching maintains a global view of the whole dataset. Different portions of this view are cached in varied granularity based on the probabilities of being accessed in the future queries. The data objects with very high access probabilities are cached in the finest granularity, i.e., the data objects themselves. The data objects which are less likely to be accessed in the near future are abstracted and logically cached in the form of complementary regions (CRs) in a coarse granularity. CS caching naturally supports all types of location-based queries. In this paper, we explore several design and system issues of CS caching, including cache memory allocation between objects and CRs, and CR coalescence. We develop algorithms for location-based queries and a cache replacement mechanism. Through an extensive performance evaluation, we show that CS caching is superior to existing caching schemes for location-based services.

title: Semantic small world: an overlay network for peer-to-peer search
abstract: For a peer-to-peer (P2P) system holding massive amount of data, efficient semantic based search for resources (such as data or services) is a key determinant to its scalability. This work presents the design of an overlay network, namely semantic small world (SSW), that facilitates efficient semantic based search in P2P systems. SSW is based on three innovative ideas: 1) small world network; 2) semantic clustering; 3) dimension reduction. Peers in SSW are clustered according to the semantics of their local data and self-organized as a small world overlay network. To address the maintenance issue of high dimensional overlay networks, a dynamic dimension reduction method, called adaptive space linearization, is used to construct a one-dimensional SSW that supports operations in the high dimensional semantic space. SSW achieves a very competitive trade-off between the search latencies/traffic and maintenance overheads. Through extensive simulations, we show that SSW is much more scalable to very large network sizes and very large numbers of data objects compared to pSearch, the state-of-the-art semantic-based search technique for P2P systems. In addition, SSW is adaptive to distribution of data and locality of interest; is very resilient to failures; and has good load balancing property.

title: Search K Nearest Neighbors on Air
abstract: While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile devices make this problem particularly interesting and challenging. An efficient data organization, called sorted list, and the corresponding search algorithm are proposed and compared with the well-known spatial index, R-Tree. In addition, we develop an approximate search scope to guide the search at the very beginning of the search process and a learning algorithm to adapt the search scope during the search to improve energy and access efficiency. Simulation based performance evaluation is conducted to compare sorted list and R-Tree. The results show that the utilization of search scope and learning algorithm improves search efficiency of both index mechanisms significantly. While R-Tree is more power efficient when a large number of nearest neighbors is requested, the sorted list has better access efficiency and less power consumption when the number of nearest neighbors is small.

title: Energy-Conserving Air Indexes for Nearest Neighbor Search
abstract: A location-based service (LBS) provides information based on the location information specified in a query. Nearest-neighbor (NN) search is an important class of queries supported in LBSs. This paper studies energy-conserving air indexes for NN search in a wireless broadcast environment. Linear access requirement of wireless broadcast weakens the performance of existing search algorithms designed for traditional spatial database. In this paper, we propose a new energy- conserving index, called grid-partition index, which enables a single linear scan of the index for any NN queries. The idea is to partition the search space for NN queries into grid cells and index all the objects that are potential nearest neighbors of a query point in each grid cell. Three grid partition schemes are proposed for the grid-partition index. Performance of the proposed grid-partition indexes and two representative traditional indexes (enhanced for wireless broadcast) is evaluated using both synthetic and real data. The result shows that the grid-partition index substantially outperforms the traditional indexes.

title: JAM: Java Agents for Meta-Learning over Distributed Databases
abstract: In this paper, we describe the JAM system, a distributed, scalable and portable agent-based data mining system that employs a general approach to scaling data mining applications that we call meta-learning. JAM provides a set of learning programs, implemented either as JAVA applets or applications, that compute models over data stored locally at a site. JAM also provides a set of meta-learning agents for combining multiple models that were learned (perhaps) at different sites. It employs a special distribution mechanism which allows the migration of the derived models or classifier agents to other remote sites. We describe the overall architecture of the JAM system and the specific implementation currently under development at Columbia University. One of JAM's target applications is fraud and intrusion detection in financial information systems. A brief description of this learning task and JAM's applicability are also described. Interested users may download JAM from http://www.cs.columbia.edu/~sal/JAM/PROJECT.

title: Nearest Surrounder Queries
abstract: In this paper, we present a new type of spatial queries called Nearest Surrounder (NS) queries. An NS query determines the nearest polygon-shaped spatial objects (referred to as nearest surrounder objects) and their orientations with respect to a query point from an object set. Besides, we derive two NS query variants, namely, multitier NS (m-NS) queries and angle-constrained NS (ANS) queries. An m-NS query searches multiple layers of NS objects for the same range of angles from a query point. An ANS query searches for NS objects within a specified range of angles. To evaluate NS queries and their variants, we explore angle-based and distance-based bound properties of polygons, and devise two efficient algorithms, namely, Sweep and Ripple, based on R-tree. The algorithms access objects in an order according to their orientations and distances with respect to a given query point, respectively. They are efficient as they can finish a search with one index lookup. Besides, they can progressively deliver a query result. Through empirical studies, we evaluate the proposed algorithms and report their performance for both synthetic and real object sets.

title: An effective hardware architecture for bump mapping using angular operation
abstract: In this paper, we propose an effective bump mapping algorithm that utilizes the reference space with the polar coordinate system and also propose a new hardware architecture associated with the proposed bump mapping algorithm. The proposed architecture reduces the computations to transform the vectors from the object space into the reference space by using a new vector rotation method. It also reduces the computations for the illumination calculation by using the law of cosine. Compared with the previous approaches, the proposed architecture reduces multiplication operations up to 78%.

title: The Importance of Convexity in Learning with Squared Loss
abstract: We show that if the closure of a function class F under the metric induced by some probability distribution is not convex, then the sample complexity for agnostically learning F with squared loss (using only hypotheses in F) is /spl Omega/(ln(1//spl delta/)//spl epsiv//sup 2/) where 1-/spl delta/ is the probability of success and /spl epsiv/ is the required accuracy. In comparison, if the class F is convex and has finite pseudodimension, then the sample complexity is O(1//spl epsiv/(ln(1//spl epsiv/)+ln(1/b)). If a nonconvex class F has finite pseudodimension, then the sample complexity for agnostically learning the closure of the convex hull of F, is O(1//spl epsiv/(1//spl epsiv/(ln(1//spl epsiv/)+ln(1//spl delta/)). Hence, for agnostic learning, learning the convex hull provides better approximation capabilities with little sample complexity penalty.

title: Distributed scalable location data management system based on the GALIS architecture
abstract: A challenging task in the LBS system engineering is to implement a highly scalable system architecture which can manage moderate-size configurations handling thousands of moving items as well as upper-end configurations handling millions of moving items. The architecture named the GALIS is a cluster-based distributed computing system architecture that consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone. In this paper, we explain a prototype location data management system structuring major components of GALIS by employing the TMO programming scheme, including the execution engine middleware developed to support real-time distributed object programming and real-time distributed computing system design. We present how to generate realistic location sensing reports and how to process such location reports and location-related queries. Some experimental results showing performance factors regarding distributed query processing are also explained.