Pasquale Savino

D-Index: Distance Searching Index for Metric Data Sets

In order to speedup retrieval in large collections of data, index structures partition the data into subsets so that query requests can be evaluated without examining the entire collection. As the complexity of modern data types grows, metric spaces have become a popular paradigm for similarity retrieval. We propose a new index structure, called D-Index, that combines a novel clustering technique and the pivot-based distance searching strategy to speed up execution of similarity range and nearest neighbor queries for large files with objects stored in disk memories. We have qualitatively analyzed D-Index and verified its properties on actual implementation. We have also compared D-Index with other index structures and demonstrated its superiority on several real-life data sets. Contrary to tree organizations, the D-Index structure is suitable for dynamic environments with a high rate of delete/insert operations.

Approximate Similarity Retrieval with M-trees

Abstract. Motivated by the urgent need to improve the efficiency of similarity queries, approximate similarity retrieval is investigated in the environment of a metric tree index called the M-tree. Three different approximation techniques are proposed, which show how to forsake query precision for improved performance. Measures are defined that can quantify the improvements in performance efficiency and the quality of approximations. The proposed approximation techniques are then tested on various synthetic and real-life files. The evidence obtained from the experiments confirms our hypothesis that a high-quality approximated similarity search can be performed at a much lower cost than that needed to obtain the exact results. The proposed approximation techniques are scalable and appear to be independent of the metric used. Extensions of these techniques to the environments of other similarity search indexes are also discussed.

Implementing ranking strategies using text signatures

Signature files provide an efficient access method for text in documents, but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process of plausible inference. The simplest way of implementing this retrieval process is to rank documents in order of their probability of relevance. In this paper techniques are described for implementing probabilistic ranking strategies with sequential and bit-sliced signature tiles and the limitations of these implementations with regard to their effectiveness are pointed out. A detailed comparison is made between signature-based ranking techniques and ranking using term-based document representatives and inverted files. The comparison shows that term-based representations are at least competitive (in terms of efficiency) with signature files and, in some situations, superior.

Milos: A Multimedia Content Management System for Digital Library Applications

This paper describes the MILOS Multimedia Content Management System: a general purpose software component tailored to support design and effective implementation of digital library applications. MILOS supports the storage and content based retrieval of any multimedia documents whose descriptions are provided by using arbitrary metadata models represented in XML. MILOS is flexible in the management of documents containing different types of data and content descriptions; it is efficient and scalable in the storage and content based retrieval of these documents. The paper illustrates the solutions adopted to support the management of different metadata descriptions of multimedia documents in the same repository, and it illustrates the experiments performed by using the MILOS system to archive documents belonging to four different and heterogenous collections which contain news agencies, scientific papers, and audio/video documentaries.

MI-File: using inverted files for scalable approximate similarity search

We propose a new efficient and accurate technique for generic approximate similarity searching, based on the use of inverted files. We represent each object of a dataset by the ordering of a number of reference objects according to their distance from the object itself. In order to compare two objects in the dataset, we compare the two corresponding orderings of the reference objects. We show that this representation enables us to use inverted files to obtain very efficiently a very small set of good candidates for the query result. The candidate set is then reordered using the original similarity function to obtain the approximate similarity search result. The proposed technique performs several orders of magnitude better than exact similarity searches, still guaranteeing high accuracy. To also demonstrate the scalability of the proposed approach, tests were executed with various dataset sizes, ranging from 200,000 to 100 million objects.

Similarity join in metric spaces

Similarity join in distance spaces constrained by the metric postulates is the necessary complement of more famous similarity range and the nearest neighbors search primitives. However, the quadratic computational complexity of similarity joins prevents from applications on large data collections. We first study the underlying principles of such joins and suggest three categories of implementation strategies based on filtering, partitioning, or similarity range searching. Then we study an application of the D-index to implement the most promising alternative of range searching. Though also this approach is not able to eliminate the intrinsic quadratic complexity of similarity joins, significant performance improvements are confirmed by experiments.

Similarity search in metric databases through hashing

A novel access structure for similarity search in metric databases, called Similarity Hashing (SH), is proposed. It is a multi-level hash structure, consisting of search-separable bucket sets on each level. The structure supports easy insertion and bounded search costs, because at most one bucket needs to be accessed at each level for range queries up to a pre-defined value of search radius. At the same time, the pivot-based strategy significantly reduces the number of distance computations. Contrary to tree organizations, the SH structure is suitable for distributed and parallel implementations.

Processing M-trees with parallel resources

The problem of the design and implementation of parallel metric tree indexes, called M-trees, is elaborated. Four different object declustering techniques are proposed and tested in order to get a sufficient evidence needed for specifying the pros and cons of their application. In general, the obtained I/O speedup and scaleup levels are high. A method for dealing with the CPU parallelism is also proposed and its speedup and scaleup experimentally tested.

An Approach to a Content-Based Retrieval ofMultimedia Data

This paper presents a data model tailored for multimedia data representation, along with the main characteristics of a Multimedia Query Language that exploits the features of the proposed model. The model addresses data presentation, manipulation and content-based retrieval. It consists of three parts: a Multimedia Description Model, which provides a structural view of raw multimedia data, a Multimedia Presentation Model, and a Multimedia Interpretation Model which allows semantic information to be associated with multimedia data. The paper focuses on the structuring of a multimedia data model which provides support for content-based retrieval of multimedia data. The Query Language is an extension of a traditional query language which allows restrictions to be expressed on features, concepts, and the structural aspects of the objects of multimedia data and the formulation of queries with imprecise conditions. The result of a query is an approximate set of database objects which partially match such a query.

Image query processing based on multi-level signatures

This paper describes the processing of queries, expressing conditions on the content of images, in large image databases. The query language assumes that a semantic interpretation of the image content is available (i.e. an image symbolic interpretation), as result of an image analysis process. The image query language addresses important aspects of the image interpretations resulting from image analysis, by defining partial conditions on the composition of the complex objects, requirements on tkir degree of recognition, and requirements on their position in tk image interpretation. Particukzr emphasis is given on the definition of suitable content-based access structures to make more ejjicient the query processing. An approach based on multi-level signatures is adopted. The query is pre-processed on the signatures to jilter-out most of the images not sati~ing the query. Finally, an evaluation of the ejjiciency and precision of the signature technique is given.