Filippo Tangorra

Hybrid methodology for data warehouse conceptual design by UML schemas

Context: Data warehouse conceptual design is based on the metaphor of the cube, which can be derived from either requirement-driven or data-driven methodologies. Each methodology has its own advantages. The first allows designers to obtain a conceptual schema very close to the user needs but it may be not supported by the effective data availability. On the contrary, the second ensures a perfect traceability and consistence with the data sources-in fact, it guarantees the presence of data to be used in analytical processing-but does not preserve from missing business user needs. To face this issue, the necessity emerged in the last years to define hybrid methodologies for conceptual design. Objective: The objective of the paper is to use a hybrid methodology based on different multidimensional models in order to gather all advantages of each of them. Method: The proposed methodology integrates the requirement-driven strategy with the data-driven one, in that order, possibly performing alterations of functional dependencies on UML multidimensional schemas reconciled with data sources. Results: As case study, we illustrate how our methodology can be applied to the university environment. Furthermore, we evaluate quantitatively the benefits of this methodology by comparing it with some popular and conventional methodologies. Conclusion: In conclusion, we highlight how the hybrid methodology improves the conceptual schema quality. Finally, we outline our present work devoted to introduce automatic design techniques in the methodology on the basis of the logical programming.

Analytical profile estimation in database systems

Abstract Most parameters which constitute the statistical profile are related to the record selectivity. To estimate record selectivity factors, the nonparametric are better than parametric methods in that they make no a priori assumptions concerning the data distribution and generally provide accurate results. Nonparametric methods are classified into the usual scale-based methods , which function by the scaling of attribute ranges, and analytic methods discussed in this paper, which are scale independent. Our analytic method is based on the computation of a set of parameters, the so-called Canonical Coefficients , which enable the multivariate distribution of the data to be well known. Based on the canonical coefficients, the main parameters of database statistical profiles can be easily defined and efficiently calculated (in terms of computation time and estimation accuracy). In addition, some important applications, which are of peculiar interest to statistical database systems can be developed. Experimental results on real databases are presented which demonstrate the versatility and reliability of the analytic approach.

An Algebraic Model for Systems of Psychically Interacting Subjects

Abstract An algebraic model for human interaction via information transmission is presented and discussed. Information transmission is schematized as the exchange of logical propositions among human subjects. As a first approach two interacting subjects are considered as being probabilistic automata described in terms of Boolean functions: in particular, a l6-tuple of probabilities defines the ‘mechanism’ of a subject. The description of an experiment (carried out on more than 300 subjects in order to justify such a drastical hypothesis) follows. Starting from a peculiar definition of the law of composition of the mechanism, an ad hoc quasi-Boolean algebraic structure is introduced. (It would be possible to force it to be a Boolean algebra but this disagrees with reality). In such a way it is possible to investigate more complex structures such as groups or many-person systems. Human subjects can be clustered into classes of ‘dominant’mechanism which are correlated with the results of classic psychological test (Minnesota). Further, the model gives a possible interpretation of usual psychotherapeutic practices such as the ‘silence’ and the ‘use of paradoxes’

Logic programming for data warehouse conceptual schema validation

The current lack of a standard methodology for data warehouse design has led to have many possible lifecycles. In some of them, the validation of the data warehouse conceptual schema is a specific process that precedes the translation of such a schema into a logical one. This activity must ensure that the data warehouse to be implemented effectively allows all the analytical queries to be executed correctly. To accomplish this, the validation process takes the preliminary workload into account, that is, a set of queries defined from user requirements to obtain the typical information the users are interested in. The methodologies that perform such a validation process define some guidelines that must be manually executed by an expert. In this paper, we introduce a logic program to automate this activity, by checking a set of predefined issues with an inferential engine.

GrHyMM: a graph-oriented hybrid multidimensional model

The main methodologies for the data warehouse design are based on two approaches which are opposite and alternative each other. The one, based on the data-driven approach, aims to produce a conceptual schema mainly through a reengineering process of the data sources, while minimizing the involvement of end users. The other is based on the requirement-driven approach and aims to produce a conceptual schema only on the basis of requirements expressed by end users. As each of these approaches has valuable advantages, it is emerged the necessity to adopt a hybrid methodology which combines the best features of the two approaches. We introduce a conceptual model that is based on a graph-oriented representation of the data sources. The core of the proposed hybrid methodology is constituted by an automatic process of reengineering of data sources that produces the conceptual schema using a set of requirement-derived constraints.

Dimensional fact model extension via predicate calculus

The Dimensional Fact Model is a conceptual model that allows to design the multidimensional schema of a data warehouse. Its methodology is based on the (re)modelling of the schema of a relational database; such a schema is represented by a tree, and the (re)modelling consists of the traditional operations on graphs (as prune, graft, adding child, change parent, …). The tree is generated automatically by an algorithm, starting from the logical schema of a relational database. Nowadays, this algorithm is not able to manage many-to-many relationships, and then, it stops the tree creation when it meets this kind of relationships. In this work, we propose a novel methodology, able to overcome this algorithmic limit, via the declarative approach of the logical programming.

A BASIC MODEL OF INTERACTING SUBJECTS

Abstract In studying the problem of interaction between subjects, an approach which allows us to define in an unambiguous way the concepts of symmetrical, complementary and parallel interaction is proposed. This approach makes use of a point of view within which it is possible to develop a rational model based only on some fundamental elements of set theory and mathematical logic The model allows us to develop a probabilistic theory of change, the representation basis of which is furnished by the 16 basic set functions. The functions represent operations on two partially overlapping sets, which will be called the “worlds”, of interacting subjects.. Both an interaction test that allows a “measure” of the relation parameters and the preliminary results of a dyadic interaction experiment are presented Some remarks can be made from the results of the present experiment and from other similar ones. Among these there are evidences that the symmetrical, complementary and parallel classes of interactions belong t...

The role of the computer architecture simulator in the laboratory

In an introductory computer course , the assembly language is often used to ai d in the comprehension of the fundamental s of computer structure and organization . The assembler can also be used t o reinforce computer architecture concept s in an advanced course . Teaching strategies are used i n computer laboratory activities in order t o illustrate various concepts introduced during lectures and/or to acquire experience with low level language . Therefore, the following learning objectives can be attained with th e assembly language : skills in assembly language programming [1] ; informatio n representation and computer organization at internal level [2] ; structure d programming techniques and the top-down approach [3-4] ; problem solving, and elements of computer science [5-7] . A common tool adopted in the compute r laboratory is a computer simulator . Th e simulator structure depends on th e teaching objective . However, actua l simulators lack a feature which is common in simulation models : the possibility for the student to dynamically change th e characteristics of the simulated system i n order to verify the physical system performance . Regarding compute r organization, the simulator should provid e the user with a simple manner o f experimenting with the following capabilities : definition of a new register , definition of a new instruction type , modifying the bit dimension of a register , and so on . The manipulation of architectural components is a useful optiona l feature of the computer simulator in a n introductory course of computer organization, but it is necessary structure in an advanced computer architect,Lare course i n order to require the students to master computer architecture concepts and also some skills in architectural design . In the light of these considerations , it is opportune to reexamine the effective role of the computer architectur e simulator in laboratory activities .

Design process for Big Data Warehouses

Big Data Warehouses differ substantially from traditional data warehouses in that their schema should be based on novel logical models that allow more flexibility than that the relational model does. Furthermore, their design methodology also requires new principles, such as automation and agile techniques, in order to gain both a fast realization and reaction to changes in business requirements. In the paper, we discuss the new features of big data warehouses and present an innovative design methodology based on the key-value model.

Automated generation of itineraries in recommender systems for tourism

Current recommender systems can support tourists in choosing travel products (accommodation, activities, means of transport, etc.), in planning long trips, and in profitably spending time in a specific geographical area such as a region (or a city). In the last case, the system should be able to construct itineraries suited to the tourists interests. In this paper, a method for generating tourist itineraries in knowledge-based recommender systems is proposed. The method is based on a theoretical model that defines space-time relations among items of intangible cultural heritage (called events) and on transitive closure computation (of the relations), that is able to construct chains of events. The proposed method has been implemented in the T-Path recommender system, that suggests itineraries of cultural events occurring in the Apulia region.