The constantly increasing rate at which scientific papers are published makes it difficult for researchers to identify papers that currently impact the research field of their interest. In this work, we present a method that ranks papers based on their estimated short-term impact, as measured by the number of citations received in the near future. Our method models a researcher exploring the paper citation network, and introduces an attention-based mechanism, akin to a time-restricted version of preferential attachment, that explicitly captures the researcher's preference to read papers which received a lot of attention recently. A detailed experimental evaluation on real citation datasets across disciplines, shows that our approach is more effective than previous work.

In-situ processing has received a great deal of attention in recent years. In in-situ scenarios, big raw data files which do not fit in main memory, must be efficiently handled on-the-fly using commodity hardware, without the overhead of a preprocessing phase or the loading of data into a database system. This paper presents RawVis, an open source data visualization system for in-situ visual exploration and analytics over big raw data. RawVis implements novel indexing schemes and adaptive processing techniques allowing users to perform efficient visual and analytics operations directly over the data files. RawVis provides real-time interaction, reporting low response time, over large data files, using commodity hardware.

As the rate at which scientific work is published continues to increase, so does the need to discern high-impact publications. In recent years, there have been several approaches that seek to rank publications based on their expected citation-based impact. Despite this level of attention, this research area has not been systematically studied. Past literature often fails to distinguish between short-term impact, the current popularity of an article, and long-term impact, the overall influence of an article. Moreover, the evaluation methodologies applied vary widely and are inconsistent. In this work, we aim to fill these gaps, studying impact-based ranking theoretically and experimentally. First, we provide explicit definitions for short-term and long-term impact, and introduce the associated ranking problems. Then, we identify and classify the most important ideas employed by state-of-the-art methods. After studying various evaluation methodologies of the literature, we propose a specific benchmark framework that can help us better differentiate effectiveness across impact aspects. Using this framework we investigate: (1) the practical difference between ranking by short- and long-term impact, and (2) the effectiveness and efficiency of ranking methods in different settings. To avoid reporting results that are discipline-dependent, we perform our experiments using four datasets from different scientific disciplines.

There has been significant effort in recent years to explore and navigate very large linked datasets, due to the increase of their availability. Many techniques have been developed that extract the information from such datasets and present it to the user as diagrams, while others take advantage of the hierarchies of the datasets to filter and aggregate them, allowing the users to access specific information. In order to overcome the limitations regarding the volume of the presented information, we have developed a novel technique that enables the interactive visualization as one continuous graph of datasets with millions of elements. IVLG is a fully fledged prototype system that implements this technique based on a client-server architecture, enabling many users to have concurrently access to the information through a user-friendly interface. It allows the user to navigate the dataset through different levels of abstraction and locate information using innovative exploration techniques. A carefully designed storage schema along with an API that takes advantage of the appropriate indexing handles datasets with millions of elements without raising any performance issues, even when accessed from devices with limited computational resources. The proposed demonstration showcases the advantages and technical features of IVLG. A series of demonstration scenarios will show how IVLG can adapt accordingly and handle diverse real and synthetic datasets that vary on size and average node degree and how the system functionalities support the user experience and the exploration of the information.

Keyword search is the most popular querying technique on semistructured data. Keyword queries are simple and convenient. However, as a consequence of their imprecision, there is usually a huge number of candidate results of which only very few match the user’s intent. Unfortunately, the existing semantics for keyword queries are ad-hoc and they generally fail to “guess” the user intent. Therefore, the quality of their answers is poor and the existing algorithms do not scale satisfactorily. In this paper, we introduce the novel concept of cohesive keyword queries for tree data. Intuitively, a cohesiveness relationship on keywords indicates that they should form a cohesive whole in a query result. Cohesive keyword queries allow term nesting and keyword repetition. Cohesive keyword queries bridge the gap between flat keyword queries and structured queries. Although more expressive, they are as simple as flat keyword queries and not require any schema knowledge. We provide formal semantics for cohesive keyword queries and rank query results on the proximity of the keyword instances. We design a stack based algorithm which efficiently evaluates cohesive keyword queries. Our experiments demonstrate that our approach outperforms in quality previous filtering semantics and our algorithm scales smoothly on queries of even 20 keywords on large datasets.

The rapid growth of social web has contributed vast amounts of user preference data. Analyzing this data and its relationships with products could have several practical applications, such as personalized advertising, market segmentation, product feature promotion etc. In this work we develop novel algorithms for efficiently processing two important classes of queries involving user preferences, i.e. potential customers identification and product positioning. With regards to the first problem, we formulate product attractiveness based on the notion of reverse skyline queries. We then present a new algorithm, termed as RSA, that significantly reduces the I/O cost, as well as the computation cost, when compared to the state-of-the-art reverse skyline algorithm, while at the same time being able to quickly report the first results. Several real-world applications require processing of a large number of queries, in order to identify the product characteristics that maximize the number of potential customers. Motivated by this problem, we also develop a batched extension of our RSA algorithm that significantly improves upon processing multiple queries individually, by grouping contiguous candidates, exploiting I/O commonalities and enabling shared processing. Our experimental study using both real and synthetic data sets demonstrates the superiority of our proposed algorithms for the studied classes of queries.

XML query languages typically allow the specification of structural patterns using XPath. Usually, these structural patterns are in the form of trees (Tree-Pattern Queries-TPQs). Finding the occurrences of such patterns in an XML tree is a key operation in XML query evaluation. The multiple previous algorithms presented for this operation focus mainly on the evaluation of tree-pattern queries. Recently, requirements for flexible querying of XML data have motivated the consideration of query classes that are more expressive and flexible than TPQs for which efficient nonmain-memory evaluation algorithms are not known. In this paper, we consider a class of queries, called Partial Tree-Pattern Queries (PTPQs), which generalize and strictly contain TPQs. PTPQs represent a broad fragment of XPath which is very useful in practice. In order to process PTPQs, we introduce a set of sound and complete inference rules to characterize structural relationship derivation. We provide necessary and sufficient conditions for detecting query unsatisfiability and node redundancy. We also show that PTPQs can be represented as directed acyclic graphs augmented with the “same-path” constraints. In order to leverage existing efficient evaluation algorithms for less expressive classes of queries, we design two approaches that evaluate a PTPQ by decomposing it into a set of simpler queries: algorithm IndexTPQGen, exploits a structural summary of the XML data and evaluates a PTPQ by generating an equivalent set of TPQs and unioning their answers. Algorithm PartialPathJoin decomposes the PTPQ into partial-path queries, and merge-joins their solutions. We also develop PartialTreeStack, an original polynomial time holistic algorithm for PTPQs. To the best of our knowledge, this is the first algorithm to support the evaluation of such a broad structural fragment of XPath in the inverted lists evaluation model. We provide a theoretical analysis of our algorithm and identify cases where it is asymptotically optimal. An extensive experimental evaluation shows that it is more efficient, robust, and stable than the other two and it outperforms a state-of-the art XQuery engine on PTPQs.

HECATAEUS is an open-source software tool for enabling impact prediction, what-if analysis, and regulation of relational database schema evolution. We follow a graph theoretic approach and represent database schemas and database constructs, like queries and views, as graphs. Our tool enables the user to create hypothetical evolution events and examine their impact over the overall graph before these are actually enforced on it. It also allows definition of rules for regulating the impact of evolution via (a) default values for all the nodes of the graph and (b) simple annotations for nodes deviating from the default behavior. Finally, HECATAEUS includes a metric suite for evaluating the impact of evolution events and detecting crucial and vulnerable parts of the system.

Data visualization is one of the big issues of database research. OLAP as a decision support technology is highly related to the developments of data visualization area. In this paper we demonstrate how the Cube Presentation Model (CPM), a novel presentational model for OLAP screens, can be naturally mapped on the Table Lens, which is an advanced visualization technique from the Human-Computer Interaction area, particularly tailored for cross-tab reports. We consider how the user interacts with an OLAP screen and based on the particularities of Table Lens, we propose an automated proactive users support. Finally, we discuss the necessity and the applicability of advanced visualization techniques in the presence of recent technological developments. Copyright 2003 ACM.

The ESPRIT Project DWQ (Foundations of Data Warehouse Quality) aimed at improving the quality of DW design and operation through systematic enrichment of the semantic foundations of data warehousing. Logic-based knowledge representation and reasoning techniques were developed to control accuracy, consistency, and completeness via advanced conceptual modeling techniques for source integration, data reconciliation, and multi-dimensional aggregation. This is complemented by quantitative optimization techniques for view materialization, optimizing timeliness and responsiveness without losing the semantic advantages from the conceptual approach. At the operational level, query rewriting and materialization refreshment algorithms exploit the knowledge developed at design time. The demonstration shows the interplay of these tools under a shared metadata repository, based on an example extracted from an application at Telecom Italia.

We present a framework for the development of information systems based on the premise that the knowledge that influences the development process needs to somehow be captured, represented, and managed if the development process is to be rationalized. Experiences with a prototype environment developed in ESPRIT project DAIDA demonstrate the approach. The project has implemented an environment based on state-of-the-art languages for requirements modeling, design and implementation of information systems. In addition, the environment offers tools for aiding the mapping process from requirements to design and then to implementation, also for representing decisions reached during the development process. The development process itself is represented explicitly within the system, thus making the DAIDA development framework easier to comprehend, use, and modify.

An optimizing translation mechanism for the dynamic interaction between a logic-based expert system written in PROLOG and a relational database accessible through SQL is presented. The mechanism makes use of an intermediate language that decomposes the optimization problem and makes the proposed approach target-language independent. It can either facilitate expert system - database interaction, e.g., when integrating expert systems into business systems, or augment existing databases with (external) deductive capabilities.

We start with a very brief description of the many-valued logic approach to the formal treatment of null values in data bases and show some problems that are encountered. In the second part of the paper we present our approach, based on the Denotational Semantics Theory. An informal introduction to the theory is given and then the relational model is described in terms of the theory. Query evaluations are defined as continuous functions and several examples are presented. The formalization in the framework of Denotational Semantics allows for a better understanding of the semantic problems with null values. It also gives flexibility for alternatives in acceptable semantic interpretations. We conclude with a presentation of an algorithm for the evaluation of simple queries. This algorithm uses symbolic manipulations and it is more economical than the strict application of the definition of query evaluations.