Spatial skills, in particular mental rotation, are increasingly linked to STEM achievements and to computational thinking (CT) abilities. Traditional 2D spatial assessments face validity and equity issues from visual ambiguity, occlusion, and missing depth cues, requiring cognitively demanding 2D-to-3D interpretation. These artifacts might contribute to gender disparities, giving males an advantage that may reflect test bias rather than true ability differences. Immersive Virtual Reality (VR) seems to mitigate these issues by providing stereoscopic depth, multi-perspective viewing, realistic lighting, and embodied interaction. We compared spatial performance in VR and traditional 2D assessments using the Virtual Reality Mental Rotation Assessment (VRMRA) in a within-subjects study of 48 adolescents (ages 12-16). VR improved performance, yielding higher accuracy in Mental Rotation Test (MRT)-style tasks (+1.3 items, p < .001), while PSVT:R accuracy did not differ significantly between 2D and VR. We observed that gender effects are task-specific: VR improved the performance of females most in MRT-style tasks, reversing the 2D male advantage, whereas males gained more in PSVT:R-style tasks. Spatial scores correlated positively with CT across both 2D and VR assessments, indicating a link to computational problem-solving. These results suggest that the assessment of spatial skills in VR produces distinct performance patterns with respect to traditional 2D assessment, highlighting the importance of immersive VR for the development of improved tools and its potential implications for inclusive STEM education and talent identification.

Database education is a cornerstone under many of the more popular topics in computer science such as machine learning and visualization. Although, in recent years, more fundamental research into database education has come out, there are many more ways in which it can be extended. Research on the practice of teaching databases, namely on the educational materials and explanations of teachers, can help us create new building blocks for fundamental research. This working group aims to collect and present notional machines of different types, for a wide range of database subtopics. These materials offer and updated context for database educators to design their courses from, as well as open up pathways of further research into database education.

As the importance of concurrent and multithreaded programming continues to grow, many universities have incorporated these concepts into their introductory courses. Sonic Pi, a programming language designed for music creation, provides valuable support for exploring concurrency due to its simplified multithreading abstractions and its domain-specific nature. In this paper, we outline several teaching experiments aimed at undergraduate computer science students, using an interdisciplinary pedagogical approach that introduces concurrency early using Sonic Pi. The activities consist of code comprehension and code composition tasks in a collaborative learning environment. Our primary research goal is to explore and discuss students’ misconceptions about concurrency, and then draw some preliminary considerations and connections to analogous misconceptions in traditional concurrent programming languages.

As the importance of concurrent and multi-threaded programming continues to grow, many universities have incorporated these concepts into their introductory courses. Sonic Pi, a programming language designed for music creation, provides valuable support for exploring concurrency due to its simplified multi-threading abstractions and its domain-specific nature. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi provides great support for "playing'' with concurrency, and "hearing'' common problems such as data races and lack of synchronization among different threads. Our primary research goal is to explore whether the use of Sonic Pi can support students, especially in the early stages, to understand concurrent programming concepts and help them face misconceptions identified in the concurrency education literature. The approach has been applied in teaching experiments with undergraduate students involving 184 participants.

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.

A wide range of tools and applications have been developed for supporting Computer Science Education, ranging from visual programming languages to web applications. In this setting it is crucial to model user needs and provide personalized support to improve the effectiveness and satisfaction of learning experiences. This summary gives a brief overview of the workshop Adaptation and Personalization in Computer Science Education organized at UMAP 2020 in order to bring together researchers, practitioners and education stakeholders interested in these topics. The workshop program consists of a keynote speech by Wolfgang Slany head of the Catrobat Project and by three technical sessions offering different perspectives on the main themes of the workshop.

Recent technological developments in the fields of positioning and mobile communications gave rise to the availabilityof massive spatio-temporal open datasets about cities. A proper exploitation of these big datasets by decision makers of smart cities could be very useful to analyse and understand mobility patterns, with the final goal of easing many transportation problems, like parking search and traffic. While many research efforts have been aimed at defining powerful visual analytics tools for exploring vehicular trajectory data, to date almost no specifically tailored tools are available to analyse (on-street) parking data and dynamics. To fill this gap, in this paper we present the current state of an on-going research on the development of a visual analytics tool, meant to support decision makers of smart cities in performing multigranular spatio-temporal explorations of mobility open data, like those about parking. Moreover, the proposed GUI offers the possibility to overlay external spatio-temporal datasets as well as to customize the way this data is rendered, to get a better insight on the parking dynamics and its influencing factors.

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.

One of the claimed advantages of Model-driven development is the improvement in maintainability. However, few studies consider this aspect from an empirical point of view. This paper reports the results of a controlled experiment with 21 bachelor students aimed at investigating the effectiveness of Model-driven development during software maintenance and evolution activities. The tool used in the experiment is UniMod, a specific implementation of executable UML. Preliminary results indicate a relevant shortening of time with no significant impact on correctness, gained through the use of UniMod instead of conventional programming (i.e., code-centric programming).

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.

A well-known problem of temporal databases is that the amount of stored data tends to increase very fast. Moreover, detailed data are useful when they are acquired but they often become less relevant after some time. In most cases, after a period of time only summarized data need to be kept, whereas detailed data expire and can be removed from the database. Multigranular temporal databases enhance the expressive power of temporal databases by supporting temporal attributes at different levels of detail. However, in existing approaches the level of detail of an attribute, that is its granularity, depends only on the attribute semantics and does not depend on how recent the attribute values are. This paper proposes an approach supporting the aggregation of different portions of the value of a temporal attribute at different levels of detail, and the deletion or the transfer to tertiary storage of old values at a given level of detail, in order to minimize disk storage occupancy. In the proposed multigranular temporal object-oriented data model, the expiration of attribute values at a given granularity can be specified, together with the action to take when data expire: either aggregation to a coarser granularity, or deletion of values, or both.

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.

Abstract In this paper we propose a set‐oriented rule‐based method definition language for object‐oriented databases. Most existing object‐oriented database systems exploit a general‐purpose imperative object‐oriented programming language as the method definition language. Because methods are written in a general‐purpose imperative language, it is difficult to analyze their properties and to optimize them. Optimization is important when dealing with a large amount of objects as in databases. We therefore believe that the use of anad hoc, set‐oriented language can offer some advantages, at least at the specification level. In particular, such a language can offer an appropriate framework to reason about method properties. In this paper, besides defining a set‐oriented rule‐based language for method definition, we formally define its semantics, addressing the problems of inconsistency and non‐determinism in set‐oriented updates. Moreover, we characterize some relevant properties of methods, such as conflicts among method specifications in sibling classes and behavioral refinement in subclasses. Copyright © 2003 John Wiley & Sons, Ltd.

The main key feature of temporal databases is to maintain all values taken by object attributes over time. Since historical information may be needed at different levels of detail, multigranular temporal databases have been introduced, in which different attributes can be stored at different temporal granularities. An important issue that has not been addressed, however is that the required level of detail does not only depend on attribute semantics, rather it is often related to how recent the data are. It is quite natural that recent data are needed at greater level of detail, whereas less detail is needed as data age. As an extreme case, data can also expire, that is, they are no longer needed after a certain period of time. In this paper we address the problem of evolution and expiration of historical data in a multigranular temporal object model.

This paper deals with dimensional data. Examples of dimensions are space and time. Thus, temporal, spatial, spatiotemporal values are examples of dimensional data. We define the notion of dimensional object, extending an object-oriented ODMG-like type system to include dimensional types. We then address the problem of querying dimensional objects. Linguistic constructs are introduced that allow objects with different dimensions to be mixed in the same phrases. This allows the user to formulate both associative and navigational accesses seamlessly without having to worry about the dimensions of the various data elements involved.

Managing and relating temporal information at different time units is an important issue in many applications and research areas, among them temporal object-oriented databases. Due to the semantic richness of the object-oriented data model, the introduction of multiple temporal granularities in such a model poses several interesting issues. In particular, object-oriented query languages provide a navigational approach to data access, performed via path expressions. We present an extension to path expressions to a multi-granularity context. The syntax and semantics of the proposed path expressions are formally defined.

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.

In this paper we extend the ODMG object data model with composite objects. A composite object is an object built by aggregating other component objects. Exclusiveness and dependency constraints, as well as referential integrity, can be associated with composition relationships among objects. Our composite object model is developed in the framework of the ODMG object database standard data model, but can be used in both object-oriented and object-relational database systems. In the paper, we propose a language for defining composite objects and we define the semantics of update operations on composite objects. Keywords Object-oriented database systems, composite objects, integrity constraints, data models. INTRODUCTION Object-oriented DBMS (OODBMS) and object-relational DBMS (ORDBMS) are establishing themselves as the new generation DBMS. Object database systems overcome the limitations of relational systems with respect to several emerging data-intensive applications because of thei...

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.