An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

SQLator: An online SQL learning workbench

SQL (Structured Query Language) is one of the essential topics in foundation databases courses in higher education. Due to its apparent simple syntax, learning to use the full power of SQL can be a very difficult activity. In this paper, we introduce SQLator, which is a web-based interactive tool for learning SQL. SQLator's key function is the evaluate function, which allows a user to evaluate the correctness of his/her query formulation. The evaluate engine is based on complex heuristic algorithms. The tool also provides instructors the facility to create and populate database schemas with an associated pool of SQL queries. Currently it hosts two databases with a query pool of 300+ across the two databases. The pool is divided into 3 categories according to query complexity. The SQLator user can perform unlimited executions and evaluations on query formulations and/or view the solutions. The SQLator evaluate function has a high rate of success in evaluating the user's statement as correct (or incorrect) corresponding to the question. We will present in this paper, the basic architecture and functions of SQLator. We will further discuss the value of SQLator as an educational technology and report on educational outcomes based on studies conducted at the School of Information Technology and Electrical Engineering, The University of Queensland.

Finding negative event oriented patterns in long temporal sequences

Pattern discovery in a long temporal event sequence is of great importance in many application domains. Most of the previous work focuses on identifying positive associations among time stamped event types. In this paper, we introduce the problem of defining and discovering negative associations that, as positive rules, may also serve as a source of knowledge discovery. In general, an event-oriented pattern is a pattern that associates with a selected type of event, called a target event. As a counter-part of previous research, we identify patterns that have a negative relationship with the target events. A set of criteria is defined to evaluate the interestingness of patterns associated with such negative relationships. In the process of counting the frequency of a pattern, we propose a new approach, called unique minimal occurrence, which guarantees that the Apriori property holds for all patterns in a long sequence. Based on the interestingness measures, algorithms are proposed to discover potentially interesting patterns for this negative rule problem. Finally, the experiment is made for a real application.

Semantic caching for multiresolution spatial query processing in mobile environments

Spatial data are particularly useful in mobile environments. However, due to the low bandwidth of most wireless networks, developing large spatial database applications becomes a challenging process. In this paper, we provide the first attempt to combine two important techniques, multiresolution spatial data structure and semantic caching, towards efficient spatial query processing in mobile environments. Based on the study of the characteristics of multiresolution spatial data (MSD) and multiresolution spatial query, we propose a new semantic caching model called Multiresolution Semantic Caching (MSC) for caching MSD in mobile environments. MSC enriches the traditional three-category query processing in semantic cache to five categories, thus improving the performance in three ways: 1) a reduction in the amount and complexity of the remainder queries; 2) the redundant transmission of spatial data already residing in a cache is avoided; 3) a provision for satisfactory answers before 100% query results have been transmitted to the client side. Our extensive experiments on a very large and complex real spatial database show that MSC outperforms the traditional semantic caching models significantly

A new indexing method for high dimensional dataset

Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.

Multiresolution query optimization in an online environment

Multiresolution (or multi-scale) techniques make it possible for Web-based GIS applications to access large dataset. The performance of such systems relies on data transmission over network and multiresolution query processing. In the literature the latter has received little research attention so far, and the existing methods are not capable of processing large dataset. In this paper, we aim to improve multiresolution query processing in an online environment. A cost model for such query is proposed first, followed by three strategies for its optimization. Significant theoretical improvement can be observed when comparing against available methods. Application of these strategies is also discussed, and similar performance enhancement can be expected if implemented in online GIS applications.

Indexing text and visual features for WWW images

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index imagersquos multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partitionrsquos center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images haves similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the ldquodimensionality curserdquo existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms imagersquos text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partitionrsquos center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude.

Collaborative filtering on data streams

Collaborate Filtering is one of the most popular recommendation algorithms. Most Collaborative Filtering algorithms work with a static set of data. This paper introduces a novel approach to providing recommendations using Collaborative Filtering when user rating is received over an incoming data stream. In an incoming stream there are massive amounts of data arriving rapidly making it impossible to save all the records for later analysis. By dynamically building a decision tree for every item as data arrive, the incoming data stream is used effectively although an inevitable trade off between accuracy and amount of memory used is introduced. By adding a simple personalization step using a hierarchy of the items, it is possible to improve the predicted ratings made by each decision tree and generate recommendations in real-time. Empirical studies with the dynamically built decision trees show that the personalization step improves the overall predicted accuracy.