Scalable Probabilistic Similarity Ranking in Uncertain Databases (Technical Report)

This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that are assumed to be mutually-exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ranking position, the probability of the object falling at that ranking position. The resulting rank probability distribution can serve as input for several state-of-the-art probabilistic ranking models. Existing approaches compute this probability distribution by applying a dynamic programming approach of quadratic complexity. In this paper we theoretically as well as experimentally show that our framework reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object. Furthermore, we show how the output of our method can be used to apply probabilistic top-k ranking for the objects, according to different state-of-the-art definitions. We conduct an experimental evaluation on synthetic and real data, which demonstrates the efficiency of our approach

Event detection from click-through data via query clustering

The web is an index of real-world events and lot of knowledge can be mined from the web resources and their derivatives. Event detection is one recent research topic triggered from the domain of web data mining with the increasing popularity of search engines. In the visitor-centric approach, the click-through data generated by the web search engines is the start up resource with the intuition: often such data is event-driven. In this thesis, a retrospective algorithm is proposed to detect such real-world events from the click-through data. This approach differs from the existing work as it: (i) considers the click-through data as collaborative query sessions instead of mere web logs and try to understand user behavior (ii) tries to integrate the semantics, structure, and content of queries and pages (iii) aims to achieve the overall objective via Query Clustering. The problem of event detection is transformed into query clustering by generating clusters - hybrid cover graphs; each hybrid cover graph corresponds to a real-world event. The evolutionary pattern for the co-occurrences of query-page pairs in a hybrid cover graph is imposed for the quality purpose over a moving window period. Also, the approach is experimentally evaluated on a commercial search engine\u27s data collected over 3 months with about 20 million web queries and page clicks from 650000 users. The results outperform the most recent work in this domain in terms of number of events detected, F-measures, entropy, recall etc. --Abstract, page iv

Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Improved data retrieval from TreeBASE via taxonomic and linguistic data enrichment

Background: TreeBASE, the only data repository for phylogenetic studies, is not being used effectively since it does not meet the taxonomic data retrieval requirements of the systematics community. We show, through an examination of the queries performed on TreeBASE, that data retrieval using taxon names is unsatisfactory. Results: We report on a new wrapper supporting taxon queries on TreeBASE by utilising a Taxonomy and Classification Database (TCl-Db) we created. TCl-Db holds merged and consolidated taxonomic names from multiple data sources and can be used to translate hierarchical, vernacular and synonym queries into specific query terms in TreeBASE. The query expansion supported by TCl-Db shows very significant information retrieval quality improvement. The wrapper can be accessed at the URL http://spira.zoology.gla.ac.uk/app/tbasewrapper.php The methodology we developed is scalable and can be applied to new data, as those become available in the future. Conclusion: Significantly improved data retrieval quality is shown for all queries, and additional flexibility is achieved via user-driven taxonomy selection

Decoding billions of integers per second through vectorization

In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.Comment: For software, see https://github.com/lemire/FastPFor, For data, see http://boytsov.info/datasets/clueweb09gap

Support for taxonomic data in systematics

The Systematics community works to increase our understanding of biological diversity through identifying and classifying organisms and using phylogenies to understand the relationships between those organisms. It has made great progress in the building of phylogenies and in the development of algorithms. However, it has insufficient provision for the preservation of research outcomes and making those widely accessible and queriable, and this is where database technologies can help. This thesis makes a contribution in the area of database usability, by addressing the query needs present in the community, as supported by the analysis of query logs. It formulates clearly the user requirements in the area of phylogeny and classification queries. It then reports on the use of warehousing techniques in the integration of data from many sources, to satisfy those requirements. It shows how to perform query expansion with synonyms and vernacular names, and how to implement hierarchical query expansion effectively. A detailed analysis of the improvements offered by those query expansion techniques is presented. This is supported by the exposition of the database techniques underlying this development, and of the user and programming interfaces (web services) which make this novel development available to both end-users and programs

Exploiting contextual information in attacking set-generalized transactions

Transactions are records that contain a set of items about individuals. For example, items browsed by a customer when shopping online form a transaction. Today, many activities are carried out on the Internet, resulting in a large amount of transaction data being collected. Such data are often shared and analyzed to improve business and services, but they also contain private information about individuals that must be protected. Techniques have been proposed to sanitize transaction data before their release, and set-based generalization is one such method. In this article, we study how well set-based generalization can protect transactions. We propose methods to attack set-generalized transactions by exploiting contextual information that is available within the released data. Our results show that set-based generalization may not provide adequate protection for transactions, and up to 70% of the items added into the transactions during generalization to obfuscate original data can be detected by our methods with a precision over 80%