Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database

This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments ("General" and "Specific") and two categories of topics ("Broad" and "Narrow"). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call "Coherent Retrieval Elements". The results of our experiments show that -- when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics) -- the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.Comment: Postprint version. The editor version can be accessed through the DO

Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search

Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online

Space-efficient data structures for string searching and retrieval

Let D = {d_1, d_2, ...} be a collection of string documents of n characters in total, which are drawn from an alphabet set Sigma =[sigma] ={1,2,3,...sigma}. The top-k document retrieval problem is to maintain D as a data structure, such that when ever a query Q=(P, k) comes, we can report (the identifiers of) those k documents that are most relevant to the pattern P (of p characters). The relevance of a document d_r with respect to a pattern P is captured by score(P, d_r), which can be any function of the set of locations where P occurs in d_r. Finding the most relevant documents to the user query is the central task of any web-search engine. In the case of web-data, the documents can be demarcated along word boundaries. All the search engines use inverted index as the back-bone data structure. For each word occurring in the document collection, the inverted index stores the list of documents where it appears. It is often augmented with relevance score and/or positional information. However, when data consists of strings (e.g., in bioinformatics or Asian language texts), there are no word demarcation boundaries and the queries are arbitrary substrings instead of being proper valid words. In this case, string data structures have to be used and central approach is to use suffix tree (or string B-tree) with appropriate augmenting data structures. The work by Hon, Shah and Vitter [FOCS 2009], and Navarro and Nekrich [SODA 2012] resulted in a linear space data structure with optimal O(p+k) query time solution for this problem. This was based on geometric interpretation of the query. We extend this central problem, in two important areas of massive data sets. First, we consider an external memory disk based index, where we give near optimal results. Next, we consider compression aspects of data structure, reducing the storage space. This is central goal of the active research field of succinct data structures. We present several results, which improve upon several previous results, and are currently the best known space-time trade-offs in this area

Efficient and secure ranked multi-keyword search on encrypted cloud data

Information search and document retrieval from a remote database (e.g. cloud server) requires submitting the search terms to the database holder. However, the search terms may contain sensitive information that must be kept secret from the database holder. Moreover, the privacy concerns apply to the relevant documents retrieved by the user in the later stage since they may also contain sensitive data and reveal information about sensitive search terms. A related protocol, Private Information Retrieval (PIR), provides useful cryptographic tools to hide the queried search terms and the data retrieved from the database while returning most relevant documents to the user. In this paper, we propose a practical privacy-preserving ranked keyword search scheme based on PIR that allows multi-keyword queries with ranking capability. The proposed scheme increases the security of the keyword search scheme while still satisfying efficient computation and communication requirements. To the best of our knowledge the majority of previous works are not efficient for assumed scenario where documents are large files. Our scheme outperforms the most efficient proposals in literature in terms of time complexity by several orders of magnitude