The development of discrimination-aware solutions is one of the main current research directions in the design of automated decision systems that make deep use of people-related data. Two main groups of techniques have been designed so far: the first focuses on fair machine learning, i.e., the development of algorithms that can detect and correct the bias in the context of a learning process; the second targets approaches for detecting and mitigating bias due to the under-representation of specific groups of people in the used dataset (representation bias), independently from the further analytical tasks to be executed. In both cases, bias can originate from how and where the data was originally collected or it can be introduced, sometimes amplified, during the data preparation steps preceding any analytical task. In this paper, we focus on representation bias and we present an approach for mitigating representation bias in datasets generated through data transformation, a specific data preparation phase. Differently from similar proposals, the proposed technique can mitigate bias defined in terms of multiple types of constraints, including coverage and fairness. To show the applicability of the proposed approach, we consider dropout prediction in the educational context as a case study. The preliminary reported experimental results show that the proposed technique can mitigate representation bias in an effective and efficient way.

The increasing impact of data-based decisions in education has risen concerns about the potential risk of an amplification of educational and societal inequities already implicit in existing data. Nondiscrimination can be characterized in terms of different properties (i.e., fairness, diversity, and coverage). Research mostly focused on fair machine learning, i.e., on the development of algorithms that can detect and, in some cases, correct bias. In this paper, we consider the case of dropout prediction and focus on coverage constraints. Specifically, with reference to this case study, we show why approaches are needed for guaranteeing nondiscrimination during all the steps of the data processing pipeline and discuss how a nondiscriminating pre-processing can be ensured by relying on coverage-based constraints on data transformations.

Many solutions for coarse geolocating of users at the time they post a message exist. However, for many important applications, like traffic monitoring and event detection, finer geolocation at the level of city neighborhoods, i.e., at a sub-city level, is needed. Data-driven approaches often do not guarantee good accuracy and efficiency due to the higher number of sub-city level positions to be estimated and the low availability of balanced and large training sets. We claim that external information sources overcome limitations of data-driven approaches in achieving good accuracy for sub-city level geolocation and we present a knowledge-driven approach achieving good results once the reference area of a message is known. Our algorithm, called Sherloc, exploits toponyms in the message, extracts their semantic from a geographic gazetteer, and embeds them into a metric space that captures the semantic distance among them. We identify the semantically closest toponyms to a message and then cluster them with respect to their spatial locations. Sherloc requires no prior training, it can infer the location at sub-city level with high accuracy, and it is not limited to geolocating on a fixed spatial grid.

The Information Retrieval research has used semantics to provide accurate search results, but the analysis of conceptual abstraction has mainly focused on information integration. We consider session-based query expansion in Geographical Information Retrieval, and investigate the impact of semantic granularity (i.e., specificity of concepts representation) on the suggestion of relevant types of information to search for. We study how different levels of detail in knowledge representation influence the capability of guiding the user in the exploration of a complex information space. A comparative analysis of the performance of a query expansion model, using three spatial ontologies defined at different semantic granularity levels, reveals that a fine-grained representation enhances recall. However, precision depends on how closely the ontologies match the way people conceptualize and verbally describe the geographic space.

Source selection deserves attention for live query processing over distributed, poorly controlled data sources since it is the key to produce the best available information, in terms of relevance, trustness, and freshness, as query result. In this paper, we present an approach taking into account contextdependent data quality, according to different dimensions, during source selection, with the aim of selecting not only the most relevant but also the highest quality sources.

Schemas are often used to constrain the content and structure of XML documents. They can be quite big and complex and, thus, difficult to be accessed manually. The ability to query a single schema, a collection of schemas or to retrieve schema components that meet certain structural constraints significantly eases schema management and is, thus, useful in many contexts. In this paper, we propose a query language, named XSPath, specifically tailored for XML schema that works on logical graph-based representations of schemas, on which it enables the navigation, and allows the selection of nodes. We also propose XPath/XQuery-based translations that can be exploited for the evaluation of XSPath queries. An extensive evaluation of the usability and efficiency of the proposed approach is finally presented within the EXup system [9].

The ability of efficiently detecting changes in XML documents is crucial in many application contexts. If such changes are represented as XQuery Update Pending Update Lists (PULs), they can then be applied on documents using XQuery Update engines, and document management can take advantage of existing composition, inversion, reconciliation approaches developed in the update processing context. The paper presents an XML edit-script generator with the unique characteristic of using PULs as edit-script language and improving the state of the art from both the performance and the generated edit-script quality perspectives.

In many emerging XML application contexts and distributed execution environments (like disconnected and cloud computing, collaborative editing and document versioning) the server that determines the updates to be performed on a document, by evaluating an XQuery Update expression, is not always the same that actually makes such updates-represented as Pending Update Lists (PULs)effective. The process of generating the PUL is thus decoupled from that of making its effect persistent on the document. The PUL executor needs to manage several PULs, that, depending on the application context, are to be executed as sequential or parallel update requests, possibly relying on application-specific policies. This requires some capabilities of dynamic reasoning on updates. In the paper, we state the most relevant properties to reason on, develop the corresponding algorithms and present a PUL handling system, providing an experimental evaluation of this system.

Data on the Web mostly are in XML format and the need often arises to update their structure, commonly described by an XML Schema. When a schema is modified the effects of the modification on documents need to be faced. XSUpdate is a language that allows to easily identify parts of an XML Schema, apply a modification primitive on them and finally define an adaptation for associated documents, while Eχup is the corresponding engine for processing schema modification and document adaptation statements. Purpose of this demonstration is to provide an overview of the facilities of the XSUpdate language and of the Eχup system.

Applications that involve data integration among multiple sources often require a preliminary step of data reconciliation in order to ensure that tuples match correctly across the sources. In dynamic settings such as data mashups, however, traditional offline data reconciliation techniques that require prior availability of the data may not be applicable. The alternative, performing similarity joins at query time, is computationally expensive, while ignoring the mismatch problem altogether leads to an incomplete integration. In this paper we make the assumption that, in some dynamic integration scenarios, users may agree to trade the completeness of a join result in return for a faster computation. We explore the consequences of this assumption by proposing a novel, hybrid join algorithm that involves a combination of exact and approximate join operators, managed using adaptive query processing techniques. The algorithm is optimistic: it can switch between physical join operators multiple times throughout query processing, but it only resorts to approximate join operators when there is statistical evidence that result completeness is compromised. Our experiments show that sensible savings in join execution time can be achieved in practice, at the expense of a modest reduction in result completeness.

We extend the standard for object-oriented databases, ODMG, with reactive features, by proposing a language for specifying triggers and defining its semantics. This extension has several implications, thus we make three different specific contributions. First, the definition of a declarative data manipulation language for ODMG, which is missing in the current version of the standard; such a definition requires revisiting data manipulation in ODMG and also addressing issues related to set-oriented versus instance-oriented computation. Then, the definition of a trigger language for ODMG, unifying also the SQL:1999 proposal and providing support for trigger inheritance and overriding. Finally, the development of a formal semantics for the proposed data manipulation and trigger languages.

A large percentage of data managed by a variety of different application domains has spatiotemporal characteristics. Unfortunately, traditional geographical information systems do not allow for an easy representation of temporal aspects of spatial data. Moreover, they do not usually support the representation of data at multiple levels of granularity. In this paper we present a multigranular spatiotemporal data model. Our model extends the ODMG model with multiple spatial and temporal granularities. In particular, the model allows for an uniform management of two kinds of spatiotemporal objects: moving entities (e.g. cars, planes, etc.) and temporal maps (i.e., maps representing the change over time of a given geographic area). It also provides a framework for mapping the movement of an entity such as a car onto an underlying geographic area. The model we propose relies on a standard definition of temporal granularity. On the other hand, the representation of spatial entities at multiple granularities is obtained by applying model oriented map generalization principles. In particular, we consider a set of generalization operators that guarantee topological consistency.

An active database is a database in which some operations are automatically executed when specified events happen and particular conditions are met. Several systems supporting active rules in an object oriented data model have been proposed. However, several issues related to the integration of triggers with object oriented modeling concepts have not been satisfactorily addressed. We discuss issues related to trigger inheritance and refinement in the context of the Chimera active object oriented data model. In particular, we introduce a semantics for an active object language that takes into account trigger inheritance and supports trigger overriding. Moreover, we state conditions on trigger overriding ensuring that trigger semantics is preserved in subclasses.

A considerable research effort has been devoted in past years to query languages for temporal data in the context of both the relational and the object oriented model. Object oriented databases provide a navigational approach for data access based on object references. We investigate the navigational approach to querying object oriented databases. We formally define the notion of temporal path expression, and we address on a formal basis issues related to the correctness of such expressions. In particular, we focus on static analysis and give a set of conditions ensuring that an expression always results in a correct access at runtime.