Building data warehouses in the era of big data: an approach for scalable and flexible big data warehouses

During the last few years, the concept of Big Data Warehousing gained significant attention from the scientific community, highlighting the need to make design changes to the traditional Data Warehouse (DW) due to its limitations, in order to achieve new characteristics relevant in Big Data contexts (e.g., scalability on commodity hardware, real-time performance, and flexible storage). The state-of-the-art in Big Data Warehousing reflects the young age of the concept, as well as ambiguity and the lack of common approaches to build Big Data Warehouses (BDWs). Consequently, an approach to design and implement these complex systems is of major relevance to business analytics researchers and practitioners. In this tutorial, the design and implementation of BDWs is targeted, in order to present a general approach that researchers and practitioners can follow in their Big Data Warehousing projects, exploring several demonstration cases focusing on system design and data modelling examples in areas like smart cities, retail, finance, manufacturing, among others

DINE: A Framework for Deep Incomplete Network Embedding

Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines.Comment: 12 pages, 3 figure

DINE : a framework for deep incomplete network embedding

Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines. © 2019, Springer Nature Switzerland AG.E

Rich Counter-Examples for Temporal-Epistemic Logic Model Checking

Model checking verifies that a model of a system satisfies a given property, and otherwise produces a counter-example explaining the violation. The verified properties are formally expressed in temporal logics. Some temporal logics, such as CTL, are branching: they allow to express facts about the whole computation tree of the model, rather than on each single linear computation. This branching aspect is even more critical when dealing with multi-modal logics, i.e. logics expressing facts about systems with several transition relations. A prominent example is CTLK, a logic that reasons about temporal and epistemic properties of multi-agent systems. In general, model checkers produce linear counter-examples for failed properties, composed of a single computation path of the model. But some branching properties are only poorly and partially explained by a linear counter-example. This paper proposes richer counter-example structures called tree-like annotated counter-examples (TLACEs), for properties in Action-Restricted CTL (ARCTL), an extension of CTL quantifying paths restricted in terms of actions labeling transitions of the model. These counter-examples have a branching structure that supports more complete description of property violations. Elements of these counter-examples are annotated with parts of the property to give a better understanding of their structure. Visualization and browsing of these richer counter-examples become a critical issue, as the number of branches and states can grow exponentially for deeply-nested properties. This paper formally defines the structure of TLACEs, characterizes adequate counter-examples w.r.t. models and failed properties, and gives a generation algorithm for ARCTL properties. It also illustrates the approach with examples in CTLK, using a reduction of CTLK to ARCTL. The proposed approach has been implemented, first by extending the NuSMV model checker to generate and export branching counter-examples, secondly by providing an interactive graphical interface to visualize and browse them.Comment: In Proceedings IWIGP 2012, arXiv:1202.422

Counterexamples Revisited: Principles, Algorithms, Applications

Abstract. Algorithmic counterexample generation is a central feature of model checking which sets the method apart from other approaches such as theorem proving. The practical value of counterexamples to the verification engineer is evident, and for many years, counterexam-ple generation algorithms have been employed in model checking sys-tems, even though they had not been subject to an adequate fundamen-tal investigation. Recent advances in model checking technology such as counterexample-guided abstraction refinement have put strong em-phasis on counterexamples, and have lead to renewed interest both in fundamental and pragmatic aspects of counterexample generation. In this paper, we survey several key contributions to the subject includ-ing symbolic algorithms, results about the graph-theoretic structure of counterexamples, and applications to automated abstraction as well as software verification. Irrefutability is not a virtue of a theory (as people often think) but a vice

Witness and Counterexample Automata for ACTL

Abstract. Witnesses and counterexamples produced by model checkers provide a very useful source of diagnostic information. They are usually returned in the form of a single computation path along the model of the system. However, a single computation path is not enough to explain all reasons of a validity or a failure. Our work in this area is motivated by the application of action-based model checking algorithms to the test case generation for models formally specified with a CCS-like process algebra. There, only linear and finite witnesses and counterexamples are useful and for the given formula and model an efficient representation of the set of witnesses (counterexamples) explaining all reasons of validity (failure) is needed. This paper identifies a fragment of action computation tree logic (ACTL) that can be handled in this way. Moreover, a suitable form of witnesses and counterexamples is proposed and witness and counterex-ample automata are introduced, which are finite automata recognizing them. An algorithm for generating such automata is given.

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Ground state properties of a Tonks-Girardeau Gas in a periodic potential

In this paper, we investigate the ground-state properties of a bosonic Tonks-Girardeau gas confined in a one-dimensional periodic potential. The single-particle reduced density matrix is computed numerically for systems up to $N=265$ bosons. Scaling analysis of the occupation number of the lowest orbital shows that there are no Bose-Einstein Condensation(BEC) for the periodically trapped TG gas in both commensurate and incommensurate cases. We find that, in the commensurate case, the scaling exponents of the occupation number of the lowest orbital, the amplitude of the lowest orbital and the zero-momentum peak height with the particle numbers are 0, -0.5 and 1, respectively, while in the incommensurate case, they are 0.5, -0.5 and 1.5, respectively. These exponents are related to each other in a universal relation.Comment: 9 pages, 10 figure

Program Repair Suggestions from Graphical State-Transition Specifications

Abstract. In software engineering, graphical formalisms, like state-transition tables and automata, are very often indispensable parts of the specifications. Such a formalism usually leads to specification re-finement that maintains the simulation/bisimulation relation between an implementation and a specification. We investigate how to use formal techniques to generate suggestions for repairing a program that breaks the bisimulation relation with a graphical specification. We use state graphs as a unified representation of the program models and specifica-tions. We propose a technique that may evaluate the cost of a repair. We present a PTIME heuristic algorithm that suggests how to repair a model state graph. We then explain how to derive repair suggestions for programs from the repair for state graphs. Finally, we report our experi-ment that checks the performance of our repair algorithms and the costs of our repairs. Key words: state graph, state transition relation, repair, graph theory, cost, evaluation, equivalence, bisimulation