The rapid integration of machine learning (ML) into critical decisionmaking systems has heightened concerns over fairness, particularly regarding spatial biases often tied to sensitive socioeconomic factors. In response, we propose a model-agnostic post-processing method for spatial bias mitigation that operates without access to the original training data. Our approach formulates an optimization problem that minimizes a fairness measure robust to gerrymandering, subject to a constraint specifying the allowable deviation from the original model's performance ensuring spatial fairness while preserving accuracy. This measure has a 0–1 scale, offering an intuitive way to quantify spatial bias. Comprehensive evaluations on real-world datasets show that our framework effectively reduces spatial bias and achieves fairer outcomes with minimal performance loss, outperforming other state-of-the-art post-processing methods. This work advances spatial fairness methodologies, offering practitioners an efficient, interpretable, and adaptable post-processing solution to mitigate location-based discrimination in ML applications.

Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring and mining HINs relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present Atrapos, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. Atrapos selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that Atrapos accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios.

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 the era of data-driven science, conducting computational experiments that involve analysing large datasets using heterogeneous computational clusters, is part of the everyday routine for many scientists. Moreover, to ensure the credibility of their results, it is very important for these analyses to be easily reproducible by other researchers. Although various technologies, that could facilitate the work of scientists in this direction, have been introduced in the recent years, there is still a lack of open-source platforms that combine them to this end. In this work, we describe and demonstrate SCHeMa, an open-source platform that facilitates the execution and reproducibility of computational analysis on heterogeneous clusters, leveraging containerization, experiment packaging, workflow management, and machine learning technologies.

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.

The i4sea research project provides effective and efficient big data integration, processing and analysis technologies to deliver both real-time and historical operational snapshots of fishing vessels activity in national sea areas. This paper presents the architecture of the i4sea big data platform for sea area monitoring and analysis of fishing vessels activity and demonstrates the operation of some use-case pilot scenarios.

Investigating whether two different classifications of a population are associated, is an interesting problem in many scientific fields. For this reason, various statistical tests to reveal this type of associations have been developed, with the most popular of them being Fisher’s exact test. However it has lately been shown that in some cases this test fails to produce accurate results. An alternative approach, known as randomization tests, was introduced to alleviate this issue, however, such tests are computationally intensive. In this paper, we introduce two novel indexing approaches that exploit frequently occurring patterns in classifications to avoid performing redundant computations during the analysis. We conduct a comprehensive set of experiments using real datasets and application scenarios to show that our approaches always outperform the state-of-the-art, with one approach being faster by an order of magnitude.

Due to the rapidly increasing number of scientific articles, finding valuable work for further research has become tedious and time consuming. To alleviate this issue, search engines have used citation-based article impact ranking. However, most engines rely on very simplistic impact measures (usually the citation count) and make the problematic assumption that there is a one-size-fits-all impact measure. To address these problems, we present BIP! Finder, a search engine that facilitates the identification of valuable articles by exploiting two different impact measures, each capturing a different aspect of the article impact. In addition, BIP! Finder provides many useful features (article comparison, intuitive visualisations, article bookmarking mechanism, etc.) making it a powerful addition to the researcher's toolbox.

The Linked Data paradigm is the most common practice for publishing, sharing and managing information in the Data Web. Linkzoo is an IT infrastructure for collaborative publishing, annotating and sharing of Data Web resources, and their publication as Linked Data. In this paper, we overview LinkZoo and its main components, and we focus on the search facilities provided to retrieve and explore RDF resources. Two search services are presented: (1) an interactive, two-step keyword search service, where live natural language query suggestions are given to the user based on the input keywords and the resource types they match within LinkZoo, and (2) a keyword search service for exploring remote SPARQL endpoints that automatically generates a set of candidate SPARQL queries, i.e., SPARQL queries that try to capture user's information needs as expressed by the keywords used. Finally, we demonstrate the search functionalities through a use case drawn from the life sciences domain.

MicroRNAs (miRNAs) are small RNA molecules that target particular genes and prohibit their expression. Since many important diseases are related to the expression or non-expression of particular genes, knowing the miRNAs that affect these genes can help in finding possible treatments. In the last decade, a large amount of experimental studies trying to reveal the targets of several miRNAs has been published. A handful of curated databases that collect miRNA targets from the literature have been developed to make this information more easily available. However, due to the large number of existing published articles, maintaining these databases up-to-date is a tedious task that requires important resources. In this work we introduce TarMiner, a pipeline for automatic extraction of miRNA targets that can facilitate the curation process of databases that maintain miRNA validated targets.

MicroRNAs (miRNAs) are small RNA molecules that inhibit the expression of particular genes, a function that makes them useful towards the treatment of many diseases. Computational methods that predict which genes are targeted by particular miRNA molecules are known as target prediction methods. In this paper, we present a MapReduce-based system, termed MR-microT, for one of the most popular and accurate, but computational intensive, prediction methods. MR-microT offers the highly requested by life scientists feature of predicting the targets of ad-hoc miRNA molecules in near-real time through an intuitive Web interface.

The recent advances in the infrastructure of Geographic Information Systems (GIS), and the proliferation of GPS technology, have resulted in the abundance of geodata in the form of sequences of points of interest (POIs), waypoints, etc. We refer to sets of such sequences as route collections. In this work, we consider path queries on frequently updated route collections: given a route collection and two points nsand nt, a path query returns a path, i.e., a sequence of points, that connects nsto nt. We introduce two path query evaluation paradigms that enjoy the benefits of search algorithms (i.e., fast index maintenance) while utilizing transitivity information to terminate the search sooner. Efficient indexing schemes and appropriate updating procedures are introduced. An extensive experimental evaluation verifies the advantages of our methods compared to conventional graph-based search.

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.

Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.

Query processing techniques for XML data have focused mainly on tree-pattern queries (TPQs). However, the need for querying XML data sources whose structure is very complex or not fully known to the user, and the need to integrate multiple XML data sources with different structures have driven, recently, the suggestion of query languages that relax the complete specification of a tree pattern. In order to implement the processing of such languages in current DBMSs, their containment problem has to be efficiently solved.

Finding the occurrences of structural patterns in XML data is a key operation in XML query processing. Existing algorithms for this operation focus almost exclusively on path-patterns or tree-patterns. Requirements in flexible querying of XML data have motivated recently the introduction of query languages that allow a partial specification of path-patterns in a query. In this paper, we focus on the efficient evaluation of partial path queries, a generalization of path pattern queries. Our approach explicitly deals with repeated labels (that is, multiple occurrences of the same label in a query).

XML query languages typically allow the specification of structural patterns of elements. Finding the occurrences of such patterns in an XML tree is the key operation in XML query processing. Many algorithms have been presented for this operation. These algorithms focus mainly on the evaluation of path-pattern or tree-pattern queries. In this paper, we define a partial path-pattern query language, and we address the problem of its efficient evaluation on XML data.

The wide adoption of XML has increased the interest of the database community on tree-structured data management techniques. Querying capabilities are provided through tree-pattern queries. The need for querying tree-structured data sources when their structure is not fully known, and the need to integrate multiple data sources with different tree structures have driven, recently, the suggestion of query languages that relax the complete specification of a tree pattern. In this paper, we use a query language which allows partial tree-pattern queries (PTPQs). The structure in a PTPQ can be flexibly specified fully, partially or not at all. To evaluate a PTPQ, we exploit index graphs which generate an equivalent set of "complete" tree-pattern queries.In order to process PTPQs, we need to efficiently solve the PTPQ satisfiability and containment problems. These problems become more complex in the context of PTPQs because the partial specification of the structure allows new, non-trivial, structural expressions to be derived from those explicitly specified in a PTPQ. We address the problem of PTPQ satisfiability and containment in the absence and in the presence of index graphs, and we provide necessary and sufficient conditions for each case. To cope with the high complexity of PTPQ containment in the presence of index graphs,we study a family of heuristic approaches for PTPQ containment based on structural information extracted from the index graph in advance and on-the-fly. We implement our approaches and we report on their extensive experimental evaluation and comparison.

Nowadays, huge volumes of data, including scientific data, are organized or exported in tree-structured form. Querying capabilities are provided through tree-pattern queries. The need for integrating multiple data sources with different tree structures has driven, recently, the suggestion of query languages that relax the complete specification of a tree pattern. In this paper we adopt a query language with partially specified tree-pattern queries. A central feature of this type of queries is that the structure can be specified fully, partially, or not at all in a query. Important issues in query optimization require solving the query containment problem. We study the containment problem for partially specified tree-pattern queries. To support the evaluation of such queries, we use semantically rich constructs, called dimension graphs, which abstract structural information of the tree-structured data. We address the problem of query containment in the absence (absolute query containment) and in the presence (relative query containment) of dimension graphs, and we provide necessary and sufficient conditions for each type of query containment. We suggest a technique for relative query containment checking based on structural information extracted in advance from the dimension graph. Our approach is implemented and validated through extensive experimental evaluation.

Nowadays, huge volumes of data are organized or exported in a tree-structured form. Querying capabilities are provided through queries that are based on branching path expression. Even for a single knowledge domain structural differences raise difficulties for querying data sources in a uniform way. In this paper, we present a method for semantically querying tree-structured data sources using partially specified tree patterns. Based on dimensions which are sets of semantically related nodes in tree structures, we define dimension graphs. Dimension graphs can be automatically extracted from trees and abstract their structural information. They are semantically rich constructs that support the formulation of queries and their efficient evaluation. We design a tree-pattern query language to query multiple tree-structured data sources. A central feature of this language is that the structure can be specified fully, partially, or not at all in the queries. Therefore, it can be used to query multiple trees with structural differences. %and We study the derivation of structural expressions in queries by introducing a set of inference rules for structural expressions. We define two types of query unsatisfiability and we provide necessary and sufficient conditions for checking each of them. Our approach is validated through experimental evaluation.

The recent proliferation of XML-based standards and technologies for managing data on the Web demonstrates the need for effective and efficient management of tree-structured data. Querying tree-structured data is a challenging issue due to the diversity of the structural aspect in the same or in different trees. In this paper, we show how to evaluate queries on tree-structured data, called value trees. The formulation of these queries does not depend on the structure of a particular value tree. Our approach exploits semantic information provided by dimension graphs. Dimension graphs are semantically rich constructs that abstract the structural information of the value trees. We show how dimension graphs can be used to query efficiently value trees in the presence of structural differences and irregularities. Value trees and their dimension graphs are represented as XML documents. We present a method for transforming queries to XPath expressions to be evaluated on the XML documents. We also provide conditions for identifying strongly and weakly unsatisfiable queries. Finally, we conducted various experiments to compare our method for evaluating queries with one that does not exploit dimension graphs. Our results demonstrate the superiority of our approach.