An architecture for automatically developing secure OLAP applications from models

ContextDecision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which use specific views or cubes from the corporate DW or Data Marts, based on the multidimensional modeling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized accesses.ObjectiveIn previous work we have defined a Model-Driven based approach for developing a secure DWs repository by following a relational approach. Nevertheless, is also important to define security constraints in the metadata layer that connects the DWs repository with the OLAP tools, that is, over the same multidimensional structures that final users manage. This paper defines a proposal to develop secure OLAP applications and incorporates it into our previous approach.MethodOur proposal is composed of models and transformations. Our models have been defined using the extension capabilities from UML (conceptual model) and extending the OLAP package of CWM with security (logical model). Transformations have been defined by using a graphical notation and implemented into QVT and MOFScript. Finally, this proposal has been evaluated through case studies.ResultsA complete MDA architecture for developing secure OLAP applications. The main contributions of this paper are: improvement of a UML profile for conceptual modeling; definition of a logical metamodel for OLAP applications; and definition and implementation of transformations from conceptual to logical models, and from logical models to the secure implementation into a specific OLAP tool (SSAS).ConclusionOur proposal allows us to develop secure OLAP applications, providing a complete MDA architecture composed of several security models and automatic transformations towards the final secure implementation. Security aspects are early identified and fitted into a most robust solution that provides us a better information assurance and a saving of time in maintenance.     

Conceptual Data Modeling for Spatiotemporal Applications

Many exciting potential application areas for database technology manage time-varying, spatial information. In contrast, existing database techniques, languages, and associated tools provide little built-in support for the management of such information. The focus of this paper is on enhancing existing conceptual data models with new constructs, improving their ability to conveniently model spatiotemporal aspects of information. The goal is to speed up the data modeling process and to make diagrams easier to comprehend and maintain. Based on explicitly formulated ontological foundations, the paper presents a small set of new, generic modeling constructs that may be introduced into different conceptual data models. The ER model is used as the concrete context for presenting the constructs. The semantics of the resulting spatiotemporal ER model, STER, is given in terms of the underlying ER model. STER is accompanied by a textual counterpart, and a CASE tool based on STER is currently being implemented, using the textual counterpart as its internal representation.     

星链ER模型:一种数据仓库概念设计模型

对于流程工业中的数据仓库建设，存在一贯性质量分析的需求，即前一个工序的工艺可能影响下一个工序的产品质量．针对这种需求，提出了一种数据仓库的概念设计模型，星链ER模型．其中引入了链式事实主题结构的概念，并把度量细分为传递型度量与非传递型度量，给出了星链ER模型的图形表示法以及由概念模型到逻辑模型的转换规则．该模型可以方便地转化成多种逻辑模型如星型模式、雪花模式以及星座模式．     

The Influence of the Level of Abstraction on the Evolvability of Conceptual Models of Information Systems

In today's dynamic environments, evolvability of information systems is an increasingly important characteristic. We investigate evolvability at the analysis level, i.e. at the level of the conceptual models that are built of information systems (e.g. UML models). More specifically, we focus on the influence of the level of abstraction of the conceptual model on the evolvability of the model. Abstraction or genericity is a fundamental principle in several research areas such as reuse, patterns, software architectures and application frameworks. The literature contains numerous but vague claims that software based on abstract conceptual models has evolvability advantages. Hypotheses were tested with regard to whether the level of abstraction influences the time needed to apply a change, the correctness of the change and the structure degradation incurred. Two controlled experiments were conducted with 136 subjects. Correctness and structure degradation were rated by human experts. Results indicate that, for some types of change, abstract models are better evolvable than concrete ones. Our results provide insight into how the rather vague claims in literature should be interpreted.     

A Conceptual Model and Rule-Based Query Language for HTML

Most documents available over the Web conform to the HTML specification. Such documents are hierarchically structured in nature. The existing data models for the Web either fail to capture the hierarchical structure within the documents or can only provide a very low level representation of such hierarchical structure. How to represent and query HTML documents at a higher level is an important issue. In this paper, we first propose a novel conceptual model for HTML. This conceptual model has only a few simple constructs but is able to represent the complex hierarchical structure within HTML documents at a level that is close to human conceptualization/visualization of the documents. We also describe how to convert HTML documents based on this conceptual model. Using the conceptual model and conversion method, one can capture the essence (i.e., semistructure) of HTML documents in a natural and simple way. Based on this conceptual model, we then present a rule–based language to query HTML documents over the Internet. This language provides a simple but very powerful way to query both intra–document structures and inter–document structures and allows the query results to be restructured. Being rule–based, it naturally supports negation and recursion and therefore is more expressive than SQL–based languages. A logical semantics is also provided.     

A survey of table recognition

Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations, and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies both the decisions made by a table recognizer and the assumptions and inferencing techniques that underlie these decisions.Received: 29 May 2003, Revised: 28 October 2003, Published online: 1 April 2004 Correspondence to: Richard Zanibbi     

MEER—An EER model enhanced with structure methods

Entity relationship (ER) schemas include cardinality constraints, that restrict the dependencies among entities within a relationship type. The cardinality constraints have direct impact on the application maintenance, since insertions or deletions of entities or relationships might affect related entities. Indeed, maintenance of a system or of a database can be strengthened to enforce consistency with respect to the cardinality constraints in a schema. Yet, once an ER schema is translated into a logical database schema, or translated within a system, the direct correlation between the cardinality constraints and maintenance transactions is lost, since the components of the ER schema might be decomposed among those of the logical database schema or the target system.In this paper, a full solution to the enforcement of cardinality constraints in EER schemas is given. We extend the enhanced ER (EER) data model with structure-based update methods that are fully defined by the cardinality constraints. The structure methods are provably terminating and cardinality faithful, i.e., they do not insert new inconsistencies and can only decrease existing ones. A refined approach towards measuring the cardinality consistency of a database is introduced. The contribution of this paper is in the automatic creation of update methods, and in building the formal basis for proving their correctness.     

A conceptual database design approach based on rules and heuristics

Conceptual and logical database design are complex tasks for non-expert designers. Currently, the popular data models for conceptual and logical database design are the entity–relationship (ER) and the relational model, respectively. Logical design methodologies for relational databases have relied on mathematically rigorous approaches which are impractical, or textbook approaches which do not provide the rich constructs to capture real applications. Consequently, designers have to use their intuition to develop their own rules and heuristics. There is a need, therefore, to develop practical rules and heuristics that can be used to handle the complexity of design in real applications. This paper proposes a realistic and detailed approach for conceptual design using the ER model for relational databases. The approach is based on four rules that specify the order in which various types of relationships must be modelled, three rules that pertain to detection of derived relationships, and three heuristics based on observation of constructs in real applications. The approach is illustrated by many examples.     

Applications of a categorical framework for conceptual data modeling

For successful information systems development, conceptual data modeling is essential. Nowadays a plethora of techniques for conceptual data modeling exist. Many of these techniques lack a formal foundation and a lot of theory, e.g. concerning updates or schema transformations, is highly data model specific. As such there is a need for a unifying formal framework providing a sufficiently high level of abstraction. In this paper, focus is on the applications of such a framework defined in category theory. Well-known conceptual data modeling concepts, such as relationship types, generalization, specialization, and collection types are defined from a categorical point of view in this framework and an essential advantage is its “configurable semantics”. Features such as null values, uncertainty, and temporal behavior can be added by selecting appropriate instance categories. The addition of these features usually requires a complete redesign of the formalization in traditional set-based approaches to semantics. Applications of the framework in the context of schema transformations and improved automated modeling support are discussed. Received: 10 September 1996 / 19 February 1997     

Model transformations across views

Models of software often describe systems by a number of (partially) orthogonal views: a state machine, a class diagram, a scenario might specify different aspects of the one system to be built. Such abstract, multi-view models are the starting point for transformations into platform-specific models and finally the code. However, during these transformations it is usually not possible to keep such a neat separation into different views: the specification language of the target models might not support all such views. The target model, however, still needs to preserve the behaviour of the abstract, multi-view model. Therefore, model transformations have to be capable of moving aspects of the behaviour across views.In this paper, we study model transformations migrating aspects from state-based views (i.e., class specifications with data and methods) to protocol-based views (i.e., process specifications on orderings of methods) and vice versa. The specification languages for these two views are equipped with a joint, formal semantics which enables a proof of behaviour preservation. We consequently derive conditions for our transformations to be behaviour-preserving, where behaviour preservation is characterised by refinement.     

Foundations for object-oriented query processing

A minimal framework for an object-oriented query language standard should (1) include a formal definition of a high-level data model and the syntax and semantics of associated query languages, (2) provide the functionality of relational query languages, and (3) support proofs of correctness of transformations for logical query optimization. In this paper, a high-level conceptual model for object-oriented query processing is discussed; the model includes widely-used structural abstractions such as the isa relationship, associations (properties) between complex objects and complex objects/values, and inheritance of properties. A formal, algebraic query language for the model, inspired by relational algebra, is presented. Operators of the algebra allow queries based on values, queries that manipulate entire objects, and queries that construct new objects from existing objects/values. All queries retain connections to existing database objects, providing logical access paths to data. Each query result is a class, so the algebra has the closure property. The intensional and extensional results of query operators are summarized. Two forms of logical query optimization supported by the query algebra are outlined: algebraic transformations and classifier-based optimizations (optimizations which employ inclusion and exclusion dependencies between classes).     

Semantic and pragmatic processing in FIDO: A flexible interface for data-base operations

This article deals with a flexible natural language interface to access data stored in a relational data base. This interface may prove of great value to the less sophisticated user.The FIDO system (Flexible Interface for Database Operations) is presented; it accepts queries issued in natural language (Italian) and translates them into relational algebra operations. FIDO is composed of a parser (not described in the paper), a two-level semantic network, which (among other things) expresses the correspondence between the natural language terms and the conceptual database objects, and a translator/optimizer, which translates the conceptual query into its logical equivalent (i.e. into a query expressed in terms of stored relations and their attributes). The article describes the main characteristics of the semantic network and addresses, in greater detail, the problem of query translation and optimization.The flexibility of FIDO is due to the complete independence of the semantic knowledge source from the logical schema of the data base. In fact, the logical schema can be designed on the basis of considerations not related to the overall structure of FIDO (e.g. the presence of particular types of applications that have to be implemented in a particularly efficient way). In principle, the (relational) data base could be preexistent with respect to the adoption of FIDO, in that the data structures used by the translator/optimizer and described in this paper are able to describe the correspondence between the conceptual model of the domain and different logical schemas.     

Data Transformation in Cross-project Defect Prediction

Software metrics rarely follow a normal distribution. Therefore, software metrics are usually transformed prior to building a defect prediction model. To the best of our knowledge, the impact that the transformation has on cross-project defect prediction models has not been thoroughly explored. A cross-project model is built from one project and applied on another project. In this study, we investigate if cross-project defect prediction is affected by applying different transformations (i.e., log and rank transformations, as well as the Box-Cox transformation). The Box-Cox transformation subsumes log and other power transformations (e.g., square root), but has not been studied in the defect prediction literature. We propose an approach, namely Multiple Transformations (MT), to utilize multiple transformations for cross-project defect prediction. We further propose an enhanced approach MT+ to use the parameter of the Box-Cox transformation to determine the most appropriate training project for each target project. Our experiments are conducted upon three publicly available data sets (i.e., AEEEM, ReLink, and PROMISE). Comparing to the random forest model built solely using the log transformation, our MT+ approach improves the F-measure by 7, 59 and 43% for the three data sets, respectively. As a summary, our major contributions are three-fold: 1) conduct an empirical study on the impact that data transformation has on cross-project defect prediction models; 2) propose an approach to utilize the various information retained by applying different transformation methods; and 3) propose an unsupervised approach to select the most appropriate training project for each target project.     

A systematic review of transformation approaches between user requirements and analysis models

Model transformation is one of the basic principles of Model Driven Architecture. To build a software system, a sequence of transformations is performed, starting from requirements and ending with implementation. However, requirements are mostly in the form of text, but not a model that can be easily understood by computers; therefore, automated transformations from requirements to analysis models are not easy to achieve. The overall objective of this systematic review is to examine existing literature works that transform textual requirements into analysis models, highlight open issues, and provide suggestions on potential directions of future research. The systematic review led to the analysis of 20 primary studies (16 approaches) obtained after a carefully designed procedure for selecting papers published in journals and conferences from 1996 to 2008 and Software Engineering textbooks. A conceptual framework is designed to provide common concepts and terminology and to define a unified transformation process. This facilitates the comparison and evaluation of the reviewed papers.     

A content-search information retrieval process based on conceptual graphs

An intelligent information retrieval system is presented in this paper. In our approach, which complies with the logical view of information retrieval, queries, document contents and other knowledge are represented by expressions in a knowledge representation language based on the conceptual graphs introduced by Sowa. In order to take the intrinsic vagueness of information retrieval into account, i.e. to search documents imprecisely and incompletely represented in order to answer a vague query, different kinds of probabilistic logic are often used. The search process described in this paper uses graph transformations instead of probabilistic notions. This paper is focused on the content-based retrieval process, and the cognitive facet of information retrieval is not directly addressed. However, our approach, involving the use of a knowledge representation language for representing data and a search process based on a combinatorial implementation of van Rijsbergen’s logical uncertainty principle, also allows the representation of retrieval situations. Hence, we believe that it could be implemented at the core of an operational information retrieval system. Two applications, one dealing with academic libraries and the other concerning audiovisual documents, are briefly presented.     

A Methodology for Structural Conflict Resolution in the Integration of Entity-Relationship Schemas

The integration ofinf ormation systems is becoming increasingly important. A common requirement in distributed data-intensive applications, such as data warehousing and data mining, is that the various databases involved be joined in a process called schema integration. The entity-relationship (ER) model or a variant of the ER model is often used as the common data model. To aid the schema conforming, merging and restructuring phases of the integration process, various transformations have been defined to map between various equivalent ER representations. In this paper, we describe a different approach to integrate ER schemas. We focus on the resolution of structural conflicts, that is, when related real-world concepts are modeled using different constructs in different schemas. Unlike previous work, our approach proposes to resolve the structural conflict between an entity type in one schema and an attribute in another schema and show that the other structural conflicts are automatically resolved. This reduces the manual effort required in integration. We give a detailed algorithm to transform an attribute in one schema into an equivalent entity type in another schema without any loss of semantics, that is, our transformation is both information preserving and constraint preserving.     

Design of fuzzy rule-based classifiers with semantic cointension

In computing with words (CWW), knowledge is linguistically represented and has an explicit semantics defined through fuzzy information granules. The linguistic representation, in turn, naturally bears an implicit semantics that belongs to users reading the knowledge base; hence a necessary condition for achieving interpretability requires that implicit and explicit semantics are cointensive. Interpretability is definitely stringent when knowledge must be acquired from data through inductive learning. Therefore, in this paper we propose a methodology for designing interpretable fuzzy models through semantic cointension. We focus our analysis on fuzzy rule-based classifiers (FRBCs), where we observe that rules resemble logical propositions, thus semantic cointension can be partially regarded as the fulfillment of the “logical view”, i.e. the set of basic logical laws that are required in any logical system. The proposed approach is grounded on the employment of a couple of tools: DCf, which extracts interpretable classification rules from data, and Espresso, that is capable of fast minimization of Boolean propositions. Our research demonstrates that it is possible to design models that exhibit good classification accuracy combined with high interpretability in the sense of semantic cointension. Also, structural parameters that quantify model complexity show that the derived models are also simple enough to be read and understood.     

Augmenting a conceptual model with geospatiotemporal annotations

While many real-world applications need to organize data based on space (e.g., geology, geomarketing, environmental modeling) and/or time (e.g., accounting, inventory management, personnel management), existing conventional conceptual models do not provide a straightforward mechanism to explicitly capture the associated spatial and temporal semantics. As a result, it is left to database designers to discover, design, and implement - on an ad hoc basis - the temporal and spatial concepts that they need. We propose an annotation-based approach that allows a database designer to focus first on nontemporal and nongeospatial aspects (i.e., "what") of the application and, subsequently, augment the conceptual schema with geospatiotemporal annotations (i.e., "when" and "where"). Via annotations, we enable a supplementary level of abstraction that succinctly encapsulates the geospatiotemporal data semantics and naturally extends the semantics of a conventional conceptual model. An overarching assumption in conceptual modeling has always been that expressiveness and formality need to be balanced with simplicity. We posit that our formally defined annotation-based approach is net only expressive, but also straightforward to understand and implement.     

Defining and validating metrics for assessing the understandability of entity–relationship diagrams

Database and data model evolution cause significant problems in the highly dynamic business environment that we experience these days. To support the rapidly changing data requirements of agile companies, conceptual data models, which constitute the foundation of database design, should be sufficiently flexible to be able to incorporate changes easily and smoothly. In order to understand what factors drive the maintainability of conceptual data models and to improve conceptual modelling processes, we need to be able to assess conceptual data model properties and qualities in an objective and cost-efficient manner. The scarcity of early available and thoroughly validated maintainability measurement instruments motivated us to define a set of metrics for Entity–Relationship (ER) diagrams. In this paper we show that these easily calculated and objective metrics, measuring structural properties of ER diagrams, can be used as indicators of the understandability of the diagrams. Understandability is a key factor in determining maintainability as model modifications must be preceded by a thorough understanding of the model. The validation of the metrics as early understandability indicators opens up the way for an in-depth study of how structural properties determine conceptual data model understandability. It also allows building maintenance-related prediction models that can be used in conceptual data modelling practice.     

Converting XML DTDs to UML diagrams for conceptual data integration

Extensible Markup Language (XML) is fast becoming the new standard for data representation and exchange on the World Wide Web, e.g., in B2B e-commerce. Modern enterprises need to combine data from many sources in order to answer important business questions, creating a need for integration of web-based XML data. Previous web-based data integration efforts have focused almost exclusively on the logical level of data models, creating a need for techniques that focus on the conceptual level in order to communicate the structure and properties of the available data to users at a higher level of abstraction. The most widely used conceptual model at the moment is the Unified Modeling Language (UML).

This paper presents algorithms for automatically constructing UML diagrams from XML DTDs, enabling fast and easy graphical browsing of XML data sources on the web. The algorithms capture important semantic properties of the XML data such as precise cardinalities and aggregation (containment) relationships between the data elements. As a motivating application, it is shown how the generated diagrams can be used for the conceptual design of data warehouses based on web data, and an integration architecture is presented. The choice of data warehouses and On-Line Analytical Processing as the motivating application is another distinguishing feature of the presented approach.