arXiv subjects

Davide Martinenghi

Publications and source records attributed to Davide Martinenghi.

16 recordsLinked to original sources

arXiv2025

A Tutorial On Intersectionality in Fair Rankings

We address the critical issue of biased algorithms and unfair rankings, which have permeated various sectors, including search engines, recommendation systems, and workforce management. These biases can lead to discriminatory outcomes in a data-driven world, especially against marginalized and underrepresented groups. Efforts towards responsible data science and responsible artificial intelligence aim to mitigate these biases and promote fairness, diversity, and transparency. However, most fairness-aware ranking methods singularly focus on protected attributes such as race, gender, or socio-economic status, neglecting the intersectionality of these attributes, i.e., the interplay between multiple social identities. Understanding intersectionality is crucial to ensure that existing inequalities are not preserved by fair rankings. We offer a description of the main ways to incorporate intersectionality in fair ranking systems through practical examples and provide a comparative overview of existing literature and a synoptic table summarizing the various methodologies. Our analysis highlights the need for intersectionality to attain fairness, while also emphasizing that fairness, alone, does not necessarily imply intersectionality.

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arXiv2025

Towards a visually interpretable analysis of Two-Phase Locking membership

Two-phase locking (2PL) is a consolidated policy commonly adopted by Database Management Systems to enforce serializability of a schedule. While the policy is well understood, both in its standard and in the strict version, automatically deriving a suitable tabular/graphical analysis of schedules with respect to 2PL is far from trivial, and requires several technicalities that do not straightforwardly translate to visual cues. In this paper, we delve into the details of the development of a tool for 2PL analysis.

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arXiv2025

Preference Queries over Taxonomic Domains

When composing multiple preferences characterizing the most suitable results for a user, several issues may arise. Indeed, preferences can be partially contradictory, suffer from a mismatch with the level of detail of the actual data, and even lack natural properties such as transitivity. In this paper we formally investigate the problem of retrieving the best results complying with multiple preferences expressed in a logic-based language. Data are stored in relational tables with taxonomic domains, which allow the specification of preferences also over values that are more generic than those in the database. In this framework, we introduce two operators that rewrite preferences for enforcing the important properties of transitivity, which guarantees soundness of the result, and specificity, which solves all conflicts among preferences. Although, as we show, these two properties cannot be fully achieved together, we use our operators to identify the only two alternatives that ensure transitivity and minimize the residual conflicts. Building on this finding, we devise a technique, based on an original heuristics, for selecting the best results according to the two possible alternatives. We finally show, with a number of experiments over both synthetic and real-world datasets, the effectiveness and practical feasibility of the overall approach.

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arXiv2025

Keyword Search in the Deep Web

The Deep Web is constituted by data that are accessible through Web pages, but not readily indexable by search engines as they are returned in dynamic pages. In this paper we propose a conceptual framework for answering keyword queries on Deep Web sources represented as relational tables with so-called access limitations. We formalize the notion of optimal answer, characterize queries for which an answer can be found, and present a method for query processing based on the construction of a query plan that minimizes the accesses to the data sources.

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arXiv2025

Partitioning Strategies for Parallel Computation of Flexible Skylines

While classical skyline queries identify interesting data within large datasets, flexible skylines introduce preferences through constraints on attribute weights, and further reduce the data returned. However, computing these queries can be time-consuming for large datasets. We propose and implement a parallel computation scheme consisting of a parallel phase followed by a sequential phase, and apply it to flexible skylines. We assess the additional effect of an initial filtering phase to reduce dataset size before parallel processing, and the elimination of the sequential part (the most time-consuming) altogether. All our experiments are executed in the PySpark framework for a number of different datasets of varying sizes and dimensions.

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arXiv2024

Simplified integrity checking for an expressive class of denial constraints

Data integrity is crucial for ensuring data correctness and quality, maintained through integrity constraints that must be continuously checked, especially in data-intensive systems like OLTP. While DBMSs handle common constraints well, complex constraints often require ad-hoc solutions. Research since the 1980s has focused on automatic and simplified integrity constraint checking, leveraging the assumption that databases are consistent before updates. This paper discusses using program transformation operators to generate simplified integrity constraints, focusing on complex constraints expressed in denial form. In particular, we target a class of integrity constraints, called extended denials, which are more general than tuple-generating dependencies and equality-generating dependencies. These techniques can be readily applied to standard database practices and can be directly translated into SQL.

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arXiv2024

Computing the Non-Dominated Flexible Skyline in Vertically Distributed Datasets with No Random Access

In today's data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale, decentralized data storage. These algorithms enhance data privacy by processing data locally, reducing the need for data transfer and minimizing exposure to breaches. They also improve scalability, as they can handle vast amounts of data spread across multiple locations without requiring centralized access. Top-k queries have been studied extensively under this lens, and are particularly suitable in applications involving healthcare, finance, and IoT, where data is often sensitive and distributed across various sources. Classical top-k algorithms are based on the availability of two kinds of access to sources: sorted access, i.e., a sequential scan in the internal sort order, one tuple at a time, of the dataset; random access, which provides all the information available at a data source for a tuple whose id is known. However, in scenarios where data retrieval costs are high or data is streamed in real-time or, simply, data are from external sources that only offer sorted access, random access may become impractical or impossible, due to latency issues or data access constraints. Fortunately, a long tradition of algorithms designed for the "no random access" (NRA) scenario exists for classical top-k queries. Yet, these do not cover the recent advances in ranking queries, proposing hybridizations of top-k queries (which are preference-aware and control the output size) and skyline queries (which are preference-agnostic and have uncontrolled output size). The non-dominated flexible skyline (ND) is one such proposal. We introduce an algorithm for computing ND in the NRA scenario, prove its correctness and optimality within its class, and provide an experimental evaluation covering a wide range of cases, with both synthetic and real datasets.

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arXiv2024

Parallelizing the Computation of Robustness for Measuring the Strength of Tuples

Several indicators have been recently proposed for measuring various characteristics of the tuples of a dataset -- particularly, the so-called skyline tuples, i.e., those that are not dominated by other tuples. Numeric indicators are very important as they may, e.g., provide an additional criterion to be used to rank skyline tuples and focus on a subset thereof. We concentrate on an indicator of robustness that may be measured for any skyline tuple $t$: grid resistance, i.e., how large value perturbations can be tolerated for $t$ to remain non-dominated (and thus in the skyline). The computation of this indicator typically involves one or more rounds of computation of the skyline itself or, at least, of dominance relationships. Building on recent advances in partitioning strategies allowing a parallel computation of skylines, we discuss how these strategies can be adapted to the computation of the indicator.

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arXiv2024

Optimization Strategies for Parallel Computation of Skylines

Skyline queries are one of the most widely adopted tools for Multi-Criteria Analysis, with applications covering diverse domains, including, e.g., Database Systems, Data Mining, and Decision Making. Skylines indeed offer a useful overview of the most suitable alternatives in a dataset, while discarding all the options that are dominated by (i.e., worse than) others. The intrinsically quadratic complexity associated with skyline computation has pushed researchers to identify strategies for parallelizing the task, particularly by partitioning the dataset at hand. In this paper, after reviewing the main partitioning approaches available in the relevant literature, we propose two orthogonal optimization strategies for reducing the computational overhead, and compare them experimentally in a multi-core environment equipped with PySpark.

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arXiv2024

Don't go gaga with GIGO

We revisit integrity checking in relational and deductive databases with an approach that tolerates erroneous, inconsistent data. In particular, we relax the fundamental prerequisite that, in order to apply any method for simplified integrity checking, all data must initially have integrity. As opposed to a long-standing belief, integrity in the old state before the update is not needed for a correct application of simplification methods. Rather, we show that correct simplifications preserve what was consistent across updates. We formally characterize this property, that we call inconsistency tolerance, and state its validity for some well-known methods for integrity checking.

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arXiv2024

PG-Triggers: Triggers for Property Graphs

Graph databases are emerging as the leading data management technology for storing large knowledge graphs; significant efforts are ongoing to produce new standards (such as the Graph Query Language, GQL), as well as enrich them with properties, types, schemas, and keys. In this article, we introduce PG-Triggers, a complete proposal for adding triggers to Property Graphs, along the direction marked by the SQL3 Standard. We define the syntax and semantics of PG-Triggers and then illustrate how they can be implemented on top of Neo4j, one of the most popular graph databases. In particular, we introduce a syntax-directed translation from PG-Triggers into Neo4j, which makes use of the so-called {\it APOC triggers}; APOC is a community-contributed library for augmenting the Cypher query language supported by Neo4j. We also cover Memgraph, and show that our approach applies to this system in a similar way. We illustrate the use of PG-Triggers through a life science application inspired by the COVID-19 pandemic. The main objective of this article is to introduce an active database standard for graph databases as a first-class citizen at a time when reactive graph management is in its infancy, so as to minimize the conversion efforts towards a full-fledged standard proposal.

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arXiv2023

Lecture Notes on Monadic First- and Second-Order Logic on Strings

These notes present the essentials of first- and second-order monadic logics on strings with introductory purposes. We discuss Monadic First-Order logic and show that it is strictly less expressive than Finite-State Automata, in that it only captures a strict subset of Regular Languages -- the non-counting ones. We then introduce Monadic Second-Order logic; such a logic is, syntactically, a superset of Monadic First-Order logic and captures Regular Languages exactly. We also show how to transform an automaton into a corresponding formula and vice versa. Finally, we discuss the use of logical characterizations of classes of languages as the basis for automatic verification techniques.

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arXiv2014

Determining Relevant Relations for Datalog Queries under Access Limitations is Undecidable

Access limitations are restrictions in the way in which the tuples of a relation can be accessed. Under access limitations, query answering becomes more complex than in the traditional case, with no guarantee that the answer tuples that can be extracted (aka maximal answer) are all those that would be found without access limitations (aka complete answer). The field of query answering under access limitations has been broadly investigated in the past. Attention has been devoted to the problem of determining relations that are relevant for a query, i.e., those (possibly off-query) relations that might need to be accessed in order to find all tuples in the maximal answer. In this short paper, we show that relevance is undecidable for Datalog queries.

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arXiv2013

On the difference between checking integrity constraints before or after updates

Integrity checking is a crucial issue, as databases change their instance all the time and therefore need to be checked continuously and rapidly. Decades of research have produced a plethora of methods for checking integrity constraints of a database in an incremental manner. However, not much has been said about when to check integrity. In this paper, we study the differences and similarities between checking integrity before an update (a.k.a. pre-test) or after (a.k.a. post-test) in order to assess the respective convenience and properties.

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arXiv2013

On the dependency on the size of the data when chasing under conceptual dependencies

Conceptual dependencies (CDs) are particular kinds of key dependencies (KDs) and inclusion dependencies (IDs) that precisely characterize relational schemata modeled according to the main features of the Entity-Relationship (ER) model. An instance for such a schema may be inconsistent (data violate the dependencies) and incomplete (data constitute a piece of correct information, but not necessarily all the relevant information). While undecidable under general KDs and IDs, query answering under incomplete data is known to be decidable for CDs. The known techniques are based on the chase -- a special instance, organized in levels of depth, that is a representative of all the instances that satisfy the dependencies and that include the initial instance. Although the chase generally has infinite size, query answering can be addressed by posing the query (or a rewriting thereof) on a finite, initial part of the chase. Contrary to previous claims, we show that the maximum level of such an initial part cannot be bounded by a constant that does not depend on the size of the initial instance.

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arXiv2010

Querying Incomplete Data over Extended ER Schemata

Since Chen's Entity-Relationship (ER) model, conceptual modeling has been playing a fundamental role in relational data design. In this paper we consider an extended ER (EER) model enriched with cardinality constraints, disjointness assertions, and is-a relations among both entities and relationships. In this setting, we consider the case of incomplete data, which is likely to occur, for instance, when data from different sources are integrated. In such a context, we address the problem of providing correct answers to conjunctive queries by reasoning on the schema. Based on previous results about decidability of the problem, we provide a query answering algorithm that performs rewriting of the initial query into a recursive Datalog query encoding the information about the schema. We finally show extensions to more general settings. This paper will appear in the special issue of Theory and Practice of Logic Programming (TPLP) titled Logic Programming in Databases: From Datalog to Semantic-Web Rules.

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