Spatial Index on Air
With the advent of wireless networking and personal digital devices, the population of mobile users will increase significantly. Broadcasting is particularly suitable for environments having a large number of clients. In this paper, we study the query processing of some typical location-dependent queries, such as window queries and kNN queries, in a broadcast system. To reduce clients' power consumption and provide efficient services, a transformation of the objects is applied based on Hilbert curve. Furthermore, a linear index structure is constructed and several algorithms are devised to answer spatial queries. Experiments are conducted to evaluate the performance of the proposed transformation and related algorithms. Results show that the proposed schemes outperform existing algorithms significantly.

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: 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: 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: 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: The importance of convexity in learning with squared loss
abstract: We show that if the closure of a function class under the metric induced by some probability distribution is not convex, then the sample complexity for agnostically learning with squared loss (using only hypotheses in )i s where is the probability of success and is the required accuracy. In comparison, if the class is convex and has finite pseudodimension, then the sample complexity is . If a nonconvex class has finite pseudodimension, then the sample complexity for agnostically learning the closure of the convex hull of ,i s . Hence, for agnostic learning, learning the convex hull provides better approximation capabilities with little sample complexity penalty.

title: Interfacing Oz with the PCTE OMS: A Case Study of Integrating a Legacy System with a Standard Object Management System
abstract: The integration of a legacy system and a standard Object Management System (OMS) is often a very challenging task. This paper details a case study, our experiment in interfacing Oz with the PCTE (Portable Common Tool Environment) Object Management System. Oz is a multi-user process-centered software development environment that has been under development in our lab since 1987, originally under the name Marvel. PCTE is a specification that defines a language-independent interface providing support mechanisms for software engineering environments (SEE). One of the premises of PCTE is that, in theory, an SEE such as Oz can be built (or extended) using the services provided by PCTE. The purpose of our experiment was to study how a legacy system such as Oz can be integrated into a new environment framework, e.g., PCTE. The architecture of the legacy system and the services of the framework are the key factors in the integration approach. Because Oz historically has included a native OMS, our experiment focused on modifying Oz to use the PCTE OMS, which has an open and standard API. This paper describes how several Oz components were changed to interface to the PCTE OMS. The resulting proof-of-concept hybrid system has process control and integration services provided by Oz, and data integration services provided in part by PCTE. We discuss in depth the solutions to the concurrency control problems that arise in such an environment, where Oz and PCTE use different approaches to transaction management (i.e., each has its own transaction manager). The PCTE implementation used in our experiment was the Emeraude PCTE V 12.5.1, and the Oz version was V 1.0.1.

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: Boosting the margin: A new explanation for the effectiveness of voting methods
abstract: One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance

title: DSO: Dependable Signing Overlay
abstract: Dependable digital signing service requires both high fault-tolerance and high intrusion-tolerance. While providing high fault-tolerance, existing approaches do not satisfy the high intrusion-tolerance requirement in the face of availability, confidentiality and integrity attacks. In this paper, we propose Dependable Signing Overlay (DSO), a novel server architecture that can provide high intrusion-tolerance as well as high fault-tolerance. The key idea is: replicate the key shares and make the signing servers anonymous to clients (and thus also to the would-be attackers), in addition to using threshold signing. DSO utilizes structured P2P overlay routing techniques to provide timely services to legitimate clients. DSO is intended to be a scalable infrastructure for dependable digital signing service. This paper presents the architecture and protocols of DSO, and the analytical models for reliability and security analysis. We show that, compared with existing techniques, DSO has much better intrusion-tolerance under availability, confidentiality and integrity attacks.