Ontology learning is often applied to textual data sources with the aim of identifying, extracting and representing their various data elements, as well as their semantic relationships. Ontologies are excellent instruments for the representation of knowledge about one or more domains of knowledge, which enable us to study in detail the knowledge of the domain they host. With this in mind, we devised and developed a semiautomatic ontology learning system. It was specifically oriented for discovering the knowledge contained in a set of ancient texts of culinary recipes of a monk of the 16th century. Using the system, we produced an ontology incorporating a large diversity of culinary elements and their relationships, which offer a very rich field of research of the culinary of the 16th century in Portugal – the ontology was exposed and explored using the native mechanisms of a graph database management system.

Abstract Developing analytical systems imposes several challenges related not only to the amount and heterogeneity of the involved data but also to the constant need to readapt and evolve to overcome new business challenges. Data are a determinant factor in the success of analytical and decision‐making applications, being its nature, availability, and quality, crucial aspects for planning and structuring populating analytical systems. Today's users are more demanding, requiring adaptable and flexible analytical applications, which impose serious challenges on extract‐transform‐load (ETL) systems design and development for ensuring flexible and robust data populating services, operating 24/7, and managing and processing large volumes of data. Thus, we should design and implement ETL processes using innovative and up‐to‐date approaches, having real application evidence. In this paper, we present a service‐oriented implementation for ETL design and development. We mapped and implemented some of the most conventional ETL processes in a service‐oriented architecture, to demonstrate the application and benefits that this kind of approach will provide to ETL systems project development.

One of the most important parts of a Data Warehousing System is the Extract-Transform-Load (ETL) component. It is responsible for extracting, transforming, conciliating, and loading data for supporting decision-making requirements. Usually, due to the complexity of managing heterogeneous data, this component is responsible for consuming most of the resources required for implementing a Data Warehousing System, representing a critical component that compromises the adequacy of the system. Despite their importance, the ETL development method is essentially ad-hoc, which does not always follow or embodies the best practices. With the emergence of Big Data and associated tools, script-based ETL became, even more, a common approach. In the last years, BPMN - Business Process Model and Notation - have been proposed and used to support ETL conceptual models. Still, as an expressive language, it provides different approaches for representing the same requirements. In this paper, we explore the use of BPMN for ETL conceptual modelling, analyzing existing approaches, and proposing a set of guidelines for using this notation in a more consistent way.

Within the past several years, Linked Open Data has reached a significant position in the Web, allowing for a significant increase of structured data in this domain. Although this, there are a lot to be done in order to ensure an effective way for supporting publication of connected open data. One of the strongest research and development line in this area appeals to the use of ontologies for structuring data and their relationships. Ontologies can be modeled for organizing knowledge about a domain, facilitating its sharing and reuse. As such, we have been developed an ontology especially oriented to support the organizational structure of higher education institutions in Brazil. This ontology was prepared for measuring and providing essential concepts and reference models that can be used by different stakeholders to develop and use ontologies and vocabularies for the National Higher Education Assessment System in Brazil. In this paper we present and discuss the most relevant aspects we approached during the conceptualization and development of the referred ontology.

The ETL processes can be seen as typical data-oriented workflows composed of dozens of granular tasks that are responsible for the integration of data coming from different data sources. They are one of the most important components of a data warehousing system, strongly influenced by the complexity of business requirements, their changing, and evolution. To facilitate the planning and ETL implementation, a set of patterns specially designed to map standard ETL procedures is presented. They provide a simpler and conceptual perspective that can enrich to enable the generation of execution primitives. Generic models can be built, simplifying process views and providing methods for carrying out the acquired expertise to new applications using well-proven practices. This work demonstrates the fundaments of an ETL pattern-based approach for ETL development, its configuration and validation trough a set of Alloy specifications used to express its structural constraints and behaviour.

Abstract During the last few years, many research efforts have been done to improve the design of extract, transform, and load (ETL) models systems. Still, ETL systems are considered very time‐consuming, error‐prone, and complex involving several participants from different knowledge domains. The ETL processes are one of the most important components of a data warehousing system that are strongly influenced by the complexity of business requirements, their changing and evolution. These aspects influence not only the structure of the data warehouse itself but also the structures of the data sources involved with. To minimize the negative impact of such variables, we propose the use of ETL patterns to build specific ETL packages. In this paper, we formalize this approach using the BPMN for modeling more conceptual ETL workflows, mapping them to real execution primitives through the use of a domain‐specific language that allows for the generation of specific instances that can be executed in an ETL commercial tool.

The use of software patterns is a common practice in software design, providing reusable solutions for recurring problems. Patterns represent a general skeleton used to solve common problems, providing a way to share regular practices and reduce the resources needed for implementing software systems. Data warehousing populating processes are a very particular type of software used to migrate data from one or more data sources to a specific data schema used to support decision support activities. The quality of such processes should be guarantee. Otherwise, the final system will deal with data inconsistencies and errors, compromising its suitability to support strategic business decisions. To minimize such problems, we propose a pattern-oriented approach to support ETL lifecycle, from conceptual representation to its execution primitives using a specific commercial tool. An ontology-based meta model it was designed and used for describing patterns internal specification and providing the means to support and enable its configuration and instantiation using a domain specific language.

ETL systems are responsible for populating data warehouses' storage structures with historical subject-oriented business data. To do that in the most appropriated manner ETL systems require special computational means, involving frequently the manipulation of large amounts of data that come usually from disparate information sources. Planning and designing ETL systems are very complex tasks, where the occurrence of errors is not rare. In some particular situations, this may jeopardize the successful implementation of the entire system. Therefore, ETL systems modeling is a very important activity in an ETL project, since it allows for sketching and validating different kinds of implementations accordingly all operational requirements defined. This will reduce significantly project costs originated by misinterpreted or badly mapped specifications. In this work we propose a different approach for modeling ETL systems, using ETL patterns as main systems components of an ETL system that will be used and tested in a standard simulation environment using the workflow language YAWL. The system is supported by a cooperative community of agents that are in charge to put in practice the tasks specified of a specific ETL pattern and, together, execute the entire set of tasks required by a particular ETL system package.

Usually, data warehousing populating processes are data-oriented workflows composed by dozens of granular tasks that are responsible for the integration of data coming from different data sources. Specific subset of these tasks can be grouped on a collection together with their relationships in order to form higher- level constructs. Increasing task granularity allows for the generalization of processes, simplifying their views and providing methods to carry out expertise to new applications. Well-proven practices can be used to describe general solutions that use basic skeletons configured and instantiated according to a set of specific integration requirements. Patterns can be applied to ETL processes aiming to simplify not only a possible conceptual representation but also to reduce the gap that often exists between two design perspectives. In this paper, we demonstrate the feasibility and effectiveness of an ETL pattern-based approach using task clustering, analyzing a real world ETL scenario through the definitions of two commonly used clusters of tasks: a data lookup cluster and a data conciliation and integration cluster.

From small local businesses to large multinationals, we can find a lot of initiatives in order to put their business online. E-Commerce is rapidly expanding worldwide with the emergence of new web shops, business to business services, or payment services, just to name a few. E-Commerce companies have been continuously improving their business incomes and extending their influence in the market. However, this proliferation of online business raised a very old phenomenon: fraud. Consequently, E-Commerce companies lose significant amounts of money every year due to a large diversity of fraudulent cases. This phenomenon is quite difficult to manage and handle, requiring significant efforts, both human and financial, in its identification and treatment. In this paper we present and discuss the use of signatures on the detection of potential fraud situations in an E-Commerce Web application, showing the way signatures are processed and analysed, as well as they can contribute to establish user behaviour deviation and consequently detect potential fraud situations in useful time.

ETL processes are difficult components of modern Data Warehousing architectures, which are gaining momentum thanks the renewed interest stirred-up by the emerging Big Data paradigm. Due to the complexity of modeling ETL processes, various alternatives appeared in the literature. But several limitations arise, such as the poor attention to the flexibility of models. Following the drawbacks of actual proposals, in this paper we argue that a novel approach based on a pattern-oriented and task-reusable framework may convey in lot of advantages for the ETL process (conceptual) modeling itself. Our proposal is thus represented by a pattern-oriented meta-model for supporting the conceptual modeling of ETL processes in next-generation Data Warehousing environments. The most distinctive characteristic of our framework consists in the reusability of task-oriented components, with clear advantages for the flexibility and the extendibility of the final ETL process model so-determined. To validate our proposal, we exploit the YAWL workflow modeling language, and develop a suitable pattern for validating target ETL processes.

Due to their complexity and importance on data warehousing systems development, ETL systems are being a strong research subject approaching several issues especially related to reduce implementation costs, and enhance decision-making processes abilities. Since ETL systems are intrinsically connected to business processes, they are strongly influenced by requirements changing, which impact ranges from changes in data source structures to the integration of new business rules and requirements. In this paper, we propose the use of ETL patterns not only to specify ETL systems internal behaviour but also to describe how patterns communicate and interchange data among them. In order to test and demonstrate the feasibility of our approach, we used the Reo coordination language for conceptual modelling, which provided an understandable notation and methodology with the ability to reduce implementation errors and development costs.

From simple entertainment gadgets to real world based applications, a little bit of everything is in a mobile platform. Thus, it's not a surprise that enterprise managers, decision makers or business analytics use it also to support their most regular daily business activities. Following the same fast adoption path of other applications, their analytical needs quickly appeared in their horizon as one of the most useful applications that they could bring in their “pockets”. The data structures of such applications are very large to be received by mobile platforms. Data cubes still are quite out from currently mobile platforms' data manipulation boundaries. However, a lot of efforts were developed during the last few years to provide smaller hyper cubes with the same exploitation potentialities, requiring less computational resources as disk storage and processing time, appealing to better data compression techniques or multidimensional views selection. In this paper we present the most usual techniques for compressing and processing data cubes. In order to support such approach we designed and developed the Pocket Cubes system, an application especially oriented to prepare data cubes to be used on mobile platforms.

The formalization of software patterns has proven to be very useful in software developing, improving systems communication, data interchange across platforms, and simplifying the integration of processes and data flows. Populating a data warehouse (ETL) is often a very complex task demanding significant computational resources. It faces many drawbacks during its design and implementation, involving not only large volumes of data that must be processed but also undesirable change of business requirements. All of this leads frequently to reuse significant parts of other ETL implementations, adapting data structures and processes to comply with new requirements. Additionally, we believe that it's necessary a more simply and reliable approach for ETL conceptual modelling covering the "lack of mature" of this important part of ETL development. In this paper we explored a new approach to ETL conceptual modelling using the Reo coordination language, trying to evaluate its adequacy and expressiveness on the coordination of ETL tasks. A pattern-based approach was designed to map typical operations used in real world ETL scenarios from an initial Reo specification. For demonstration purposes, we present and discuss as two case studies, a slowly changing dimension and a surrogated key pipelining processes.

Usually data warehouses store a large volume of data, keeping in their structures all information decision-makers need on their daily activities. Usually, a significant amount of this information is processed and materialized in data cubes to be explored latter using the most flexible ways that on-line analytical processing platforms dispose to do that. However, this materialization process requires a lot of computational resources (processing time and storage), provoking as well serious performance bottlenecks in analytical systems. The distribution of such analytical structures across an organization seems to be quite appellative to attenuate such annoying effects, being in most of the cases a good alternative for improving system performance without great expense. Still, a distributed computational platform use to be less expensive than a centralized one, what give it some good advantages in any solution related to data cube distribution. Following this slope appears Grid environments. As we know these can use computational resources geographically dispersed, heterogeneously and non-dedicated as it was a single a unique environment. However, in order to reach higher performance levels, Grids need proper scheduling and data so they can be able to respond effectively to the jobs that were sent to them. In this paper, we integrate a performance prediction method in a data cube distribution strategy to define a more effective allocation of data cube views in a conventional Grid environment with the ability to receive them.

This paper presents a tool, named QuerySheet, to query spreadsheets. We defined a language to write the queries, which resembles SQL, the language to query databases. This allows to write queries which are more related to the spreadsheet content than with current approaches.

Organizations have been used decisions support systems to help them to understand and to predict interesting business opportunities over their huge databases also known as data marts. OLAP tools have been used widely for retrieving information in a summarized way (cube-like) by employing customized cubing methods. The majority of these cubing methods suffer from being just data-driven oriented and not discovery-driven ones. Data marts grow quite fast, so an incremental OLAP mining process is a required and desirable solution for mining evolving cubes. In order to present a solution that covers the previous mentioned issues, we propose a cube-based mining method which can compute an incremental cube, handling concept hierarchy modeling, as well as, incremental mining of multidimensional and multilevel association rules. The evaluation study using real and synthetic datasets demonstrates that our approach is an effective OLAP mining method of evolving data marts.

This paper recommends a new approach to the detection and containment of Web crawler traverses based on clickstream data mining. Timely detection prevents crawler abusive consumption of Web server resources and eventual site contents privacy or copyrights violation. Clickstream data differentiation ensures focused usage analysis, valuable both for regular users and crawler profiling. Our platform, named ClickTips, sustains a site-specific, updatable detection model that tags Web crawler traverses based on incremental Web session inspection and a decision model that assesses eventual containment. The goal is to deliver a model flexible enough to keep up with crawling continuous evolving and that is capable of detecting crawler presence as soon as possible. We use a real-world Web site case study as a support for process description, as well as, to evaluate the accuracy of the obtained classification models and their ability for discovering previously unknown Web crawlers.

The materialization of multidimensional structures is a common way to speed up OLAP queries. Since there might be a huge number of those structures, a variety of proposals tried to select the most beneficial set, based on the profile of the queries and observing some constraints as materializing space and maintenance time, addressing a centralized storage facility. Only recently, the distributed scenario came to stage on this area, introducing the space dimension (and corresponding communication costs) into the equation to minimize costs. This paper focuses on the selection and allocation of distributed OLAP cubes, using evolutionary algorithms, having an extended aggregation lattice as framework to capture the distributed semantics. Moreover, the evaluation of the fitness of evolutionary solutions is based on cost estimation algorithms that simulate the execution of parallel tasks, using time units as cost metric

The materialization of multidimensional structures is a common way to speed up mostly aggregation queries. Its proper selection has triggered a huge variety of proposals. Any algorithm that aims to solve the cube selection problem needs a way to estimate query and maintenance costs, as minimizing costs is the objective function. This estimation is harder if a distribution of multidimensional structures is concerned, as algorithms as to deal with an additional dimension -space -translated as a set of spatially distributed storage and processing nodes, interconnected with a communication network. In this paper we present some algorithms especially designed to estimate query and maintenance cost at a MultiNode OLAP system in time units, using a distributed non-linear generalized cost model.

Presents a transaction logic (I/sub R/) formal specification of a distributed architecture for learning classifier systems (LCS) based on a blackboard as a shared database and a set of agents, an architecture that brings parallelism to the LCS functionalities and caters for the distribution of classifiers and associate processes-the DICE system. /spl Ifr//sub R/ captures the behaviour of the system in terms of state changes due to elementary updates embedded in the agent's transactions. The blackboard is viewed as an active database system and the agents as a transaction bases; that is, sets of active rules and transactions.