The explosion of published information on the Web leads to the emergence of a Web syndication paradigm, which transforms the passive reader into an active information collector. Information consumers subscribe to RSS/Atom feeds and are notified whenever a piece of news (item) is published. The success of this Web syndication now offered on Web sites, blogs, and social media, however raises scalability issues. There is a vital need for efficient real-time filtering methods across feeds, to allow users to follow effectively personally interesting information. We investigate in this paper three indexing techniques for users' subscriptions based on inverted lists or on an ordered trie. We present analytical models for memory requirements and matching time and we conduct a thorough experimental evaluation to exhibit the impact of critical workload parameters on these structures.

We present in this paper a model that we have developed for indexing and querying web pages based on their visual rendering. In this model pages are split up into a set of visual blocks. The indexing of a block takes into account its content, its visual importance and, by permeability, the indexing of neighbors blocks. A page is modeled as a directed acyclic graph. Each node is associated with a block and labeled by the coefficient of importance of this block. Each edge is labeled by the coefficient of permeability of the target node content to the source node content. Importance and permeability coefficients cannot be manually quantified. the second part of this paper, we present an experiment consisting in learning optimal permeability coefficients by gradient descent for indexing images of a web page from the text blocks of this page. The dataset is drawn from real web pages of the train and test set of the ImagEval task2 corpus. Results demonstrate an improvement of the indexing using non uniform block permeabilities.

We consider an environment where a subscription system continuously evaluates pattern-based requests over unbounded sequential data. We propose an extension of the traditional pattern-matching techniques for efficiently handling large sets of such continuous queries. This extension relies on the introduction of variables in patterns in order to augment their expressivity. Based on this extended class of parameterized queries, our main contributions are threefold. First, we define a refinement relation based on variable relaxation. Second, we use the semi-lattice structure of the set of parameterized patterns for patterns aggregation and filtering. We propose an on-line pattern aggregation algorithm so as to both reduce the cost of pattern-matching evaluation as well as to filter out sequences that cannot match any of the patterns in a subscription cluster. Finally we show, through analysis and experiments, that our technique reduces quite effectively the cost of the matching process

Many applications rely on sequence databases and use extensively pattern-matching queries to retrieve data of interest. This paper extends the traditional pattern-matching expressions to parameterized patterns, featuring variables. Parameterized patterns are more expressive and allow to define concisely regular expressions that would be very complex to describe without variables. They can also be used to express additional constraints on patterns' variables.We show that they can be evaluated without additional cost with respect to traditional techniques (e.g., the Knuth-Morris-Pratt algorithm). We describe an algorithm that enjoys low memory and CPU time requirements, and provide experimental results which illustrate the gain of the optimized solution.

This paper focuses on the optimization of the navigation through voluminous subsumption hierarchies of topics employed by Portal Catalogs like Netscape Open Directory (ODP). We advocate for the use of labeling schemes for modeling these hierarchies in order to efficiently answer queries such as subsumption check, descendants, ancestors or nearest common ancestor, which usually require costly transitive closure computations. We first give a qualitative comparison of three main families of schemes, namely bit vector, prefix and interval based schemes. We then show that two labeling schemes are good candidates for an efficient implementation of label querying using standard relational DBMS, namely, the Dewey Prefix scheme [6] and an Interval scheme by Agrawal, Borgida and Jagadish [1]. We compare their storage and query evaluation performance for the 16 ODP hierarchies using the PostgreSQL engine.

Real-scale Semantic Web applications, such as Knowledge Portals and E-Marketplaces, require the management of large volumes of metadata, i.e., information describing the available Web content and services. Better knowledge about their meaning, usage, accessibility or quality will considerably facilitate an automated processing of Web resources. The Resource Description Framework (RDF) enables the creation and exchange of metadata as normal Web data. Although voluminous RDF descriptions are already appearing, sufficiently expressive declarative languages for querying both RDF descriptions and schemas are still missing. In this paper, we propose a new RDF query language called RQL. It is a typed functional language (a la OQL) and relies on a formal model for directed labeled graphs permitting the interpretation of superimposed resource descriptions by means of one or more RDF schemas. RQL adapts the functionality of semistructured/XML query languages to the peculiarities of RDF but, foremost, it enables to uniformly query both resource descriptions and schemas. We illustrate the RQL syntax, semantics and typing system by means of a set of example queries and report on the performance of our persistent RDF Store employed by the RQL interpreter.

Spatial joins are join operations that involve spatial data types and operators. Spatial access methods are often used to speed up the computation of spatial joins. This paper addresses the issue of benchmarking spatial join operations. For this purpose, we first present a WWW-based benchmark generator to produce sets of rectangles. Using a Web browser, experimenters can specify the number of rectangles in a sample, as well as the statistical distributions of their sizes, shapes, and locations. Second, using the generator and a well-defined set of statistical models we define several tests to compare the performance of three spatial join algorithms: nested loop, scan-and-index, and synchronized tree traversal. We also added two real-life data sets from the Sequoia 2000 storage benchmark. Our results show that the relative performance of the different techniques mainly depends on the selectivity factor of the join predicate. All of the statistical models and algorithms are available on the Web, which allows for easy verification and modification of our experiments.

Structured documents (e.g., SGML) can benefit a lot from database support and more specifically from object-oriented database (OODB) management systems. This paper describes a natural mapping from SGML documents into OODB's and a formal extension of two OODB query languages (one SQL-like and the other calculus) in order to deal with SGML document retrieval.

This paper addresses the problem of relational queries processing in the domain based database machine DBMAC. A brief description of the storage organisation of the domain based DBMS is first given. Then the operating principles of the domain based data-model, called D-model, is described through some examples. The central part of the paper deals with the translation of relational queries into operations on objects of the D-model. Objects of the D-model and a set of operations on these objects are first defined : this set S of operations is shown to be complete in that any relational query can be translated into D-model operations belonging to S.Finally we give a method for processing relation queries using D-model operations.The basic advantages to be expected from a domain based physical organization of data are :1. fast equi-join execution,2. a compact representation of intermediate results.The latter should lead to efficient processing of complex queries, provided a powerful parallel physical architecture is chosen for implementation.

New file organizations based on hashing and suitable for data whose volume may vary rapidly recently appeared in the literature. In the three schemes which have been independently proposed, rehashing is avoided, storage space is dynamically adjusted to the number of records actually stored, and there are no overflow records. Two of these techniques employ an index to the data file. Retrieval is fast and storage utilization is low. In order to increase storage utilization, we introduce two schemes based on a similar idea and analyze the performance of the second scheme. Both techniques use an index of much smaller size. In both schemes, overflow records are accepted. The price which has to be paid for the improvement in storage utilization is a slight access cost degradation.

Most data base machines use some type of "filter" to perform unary operations on files. This filter processes information "on the fly" during its transfer from mass storage (Disk) to main storage (main memory or buffer). This paper is devoted to the design study of such a filter for relational-type data bases. It is therefore assumed that data are structured as a set of n-ary relations.Using a top-down approach we classify the requirements under two catagories:1) Functional specification of the filter: the filter should perform the unary relational operations i.e. projection and restriction; it should also permit insertion deletion and updates of tuples.2) Performance requirements: in a filtering system access time is a linear function of the space used to the store relations, it becomes therefore of the utmost importance to minimize this space.In order to meet the second requirement, we suggest to structure the n-ary relations into hierarchical free format files. Clearly, such a data structure minimizes space: the remaining problems being the ability to read and update such files. It is then shown that an automaton-like device is sufficient to perform projections and restrictions of relations and insertions, deletions and updates of tuples. That device is described and an algorithm that automatically generates the microprogram realizing that device from the description of the relation and that of the operations to be performed is given.

We study new access schemes for a population of geographically distributed data users who communicate with each other and/or with a central station over a multiple-access broadcast ground radio packet-switching channel. We introduce and analyze alternating priorities (AP), round robin (RR), and random order (RO) as new conflict-free methods for multiplexing buffered users without control from a central station. These methods are effective when the number of users is not too large; as the number grows, a large overhead leads to a performance degradation. To reduce this degradation, we consider a natural extension of AP, called minislotted alternating priorities (MSAP) which reduces the overhead and is superior to fixed assignment, polling, and known random access schemes under heavy traffic conditions. At light input loads, only random access schemes outperform MSAP when we have a large population of users. In addition, and of major importance, is the fact that MSAP does not require control from a central station.

We extend the study of access schemes for packet-switched radio channels as an alternative to conventional wire communications for data transmission among users. Among the various multiple access schemes previously implemented or proposed, ALOHA presents many advantages, especially for a large population of bursty users. However, more than 60% of the ALOHA channel capacity is wasted. In this paper we introduce a separate large carrier-sensing user who "steals" slots which remain unused by the background of ALOHA users. This leads to a new multiple-access scheme: the Mixed ALOHA Carrier Sense (MACS) access scheme, whose performance We analyze. The total channel utilization is significantly increased with MACS, and the delaythroughput performance of both the large user and the background of ALOHA users is shown to be better with MACS than with a "split channel" mode in which the large user and the ALOHA users are each permanently assigned a portion of the channel.