Maribel Yasmina Santos

From Business Process Modeling to Data Model: A Systematic Approach

Business process modeling and management approaches are increasingly used and disclosed between organizations as a means of optimizing and streamlining the business activities. Among the various existing modeling languages, we stress the Business Process Model and Notation (BPMN), currently in version 2.0. BPMN is a widespread OMG standard that is actually used either in academia and in organizations. BPMN enables business process modeling, but does not facilitate the modeling of the information infrastructure involved in the process. However, interest in the data and its preservation has increased in BPMNs most recent version. The aim of this paper is to study BPMN 2.0, particularly on the usage and persistence of data, and present an approach for obtaining an early data model from the business process modeling, which may then be used as a starting data model in the software development process.

The impact of data quality in the context of pedestrian movement analysis

Positioning data sets gathered from GPS recordings of moving people or vehicles and usage logs of telecommunications networks are being increasingly used as a proxy to capture the mobility of people in a variety of places. The purpose of use of these data sets is wide-ranging and requires the development of techniques for collaborative map construction, the analysis and modelling of human behaviour, and the provision of context- aware services and applications. However, the quality of these data sets is affected by several factors depending on the technology used to collect the position and on the particular scenario where it is collected. This paper aims at assessing the quality and suitability of GPS recordings used in analysing pedestrian movement in two different recreational applications. Therefore, we look at two positioning data sets collected by two distinct groups of pedestrians, and analyse their collective movement patterns in the applications of a mobile outdoor gaming and as well as a park recreational usage. Among other findings, we show that the different reading rates of the pedestrians’ position lead to different levels of inaccuracy in the variables derived from it (e.g. velocity and bearing). This was significant in the case of bearing values that were calculated from GPS readings which, in turn, has shown a strong impact on the size of clusters of movement patterns.

Deriving a Data Model from a Set of Interrelated Business Process Models

Business process modeling and management approaches are increasingly used and disclosed between organizations as a means of optimizing and streamlining the business activities. A business process model identifies the activities, resources and data involved in the creation of a product or service, having lots of useful information that can be used to create a data model for the supporting software system. A data model is one of the most important models used in software development. Usually an organization deals with several business processes. As a consequence a software product does not usually support only one business process, but rather a set of business processes. This paper proposes an approach to generate a data model, based on a set of interrelated business processes, modeled in BPMN language. The approach allows aggregating in one data model all the information about persistent data that can be extracted from the set of business process models serving as a basis for the software development.

From Business Process Models to Use Case Models: A Systematic Approach

One of the most difficult, and crucial, activities in software development is the identification of system functional requirements. A popular way to capture and describe those requirements is through UML use case models. A business process model identifies the activities, resources and data involved in the creation of a product or service, having lots of useful information for developing a supporting software system. During system analysis, most of this information must be incorporated into use case descriptions. This paper proposes an approach to support the construction of use case models based on business process models. The proposed approach obtains a complete use case model, including the identification of actors, use cases and the corresponding descriptions, which are created from a set of predefined natural language sentences mapped from BPMN model elements.

Spatial Clustering in SOLAP Systems to Enhance Map Visualization

The emergence of the SOLAP concept supports map visualization for improving data analysis, enhancing the decision making process. However, in this environment, maps can easily become cluttered losing the benefits that triggered the appearance of this concept. In order to overcome this problem, a post-processing model is proposed, which relies on Geovisual Analytics principles. Namely, it takes advantage from the user interaction and the spatial clustering approach in order to reduce the number of elements to be visualized when this number is inadequate to a proper map analysis. Moreover, a novel heuristic to identify the threshold value from which the clusters must be generated was developed. The proposed post-processing model takes into account the query performed, i.e., the number of spatial attributes, the number of spatial dimensions, and the type of spatial objects selected from dimensions. The results obtained so far show: i the novel approach to support queries with two spatial attributes from different dimensions allows useful analysis; ii the proposed post-processing model is very effective in maintaining a map suitable to the users cognitive process; and, iii the heuristic proposed provide the user participation in the clustering process, in a user-friendly way.

A Big Data system supporting Bosch Braga Industry 4.0 strategy

Abstract People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream).

Mining geo-referenced data with qualitative spatial reasoning strategies

Abstract The process of knowledge discovery in databases aims at the discovery of associations within data sets. Data Mining is a central step of this process. It corresponds to the application of algorithms for identifying patterns in data. Mining geo-referenced data sets constitutes a special case that demands a particular approach in the knowledge discovery process. Those data sets include allusion to geographic objects, which location and extension define implicit relationships of spatial neighbourhood. The Data Mining algorithms have to take this spatial neighbourhood into account when looking for associations among data. This paper presents an approach for knowledge discovery in geo-referenced data sets in which the use of qualitative spatial reasoning strategies makes possible the discovery of patterns that are easily understood by the users. The graphical representation of the results of the knowledge discovery process also allowed a fast understanding of the results achieved.

On the Derivation of Class Diagrams from Use Cases and Logical Software Architectures

The transformation of user requirements into system requirements models can be achieved using the 4 Step Rule Set (4SRS) method that transforms UML use case diagrams into system-level object diagrams. These diagrams represent the logical architecture of the system, integrating the system-level entities, their responsibilities and the relationships among them. The logical architecture captures the system functional requirements and its non-functional intentionalities. Although contributing to the formalization of the design of software architectures, the 4SRS method needs to be extended in order to support the design of the database subsystems that may be considered pertinent within the specified logical architecture. This paper presents the extension of the 4SRS method to support the construction of the class diagram that complements the logical architecture, and shows, through the presentation of a demonstration case, the applicability of the proposed approach.

4D+SNN: A Spatio-Temporal Density-Based Clustering Approach with 4D Similarity

Spatio-temporal clustering is a sub field of data mining that is increasingly gaining more scientific attention due to the advances of location-based or environmental devices that register position, time and, in some cases, other semantic attributes. This process pretends to group objects based in their spatial and temporal similarity helping to discover interesting patterns and correlations in large data sets. One of the main challenges of this area is the ability to integrate several dimensions in a general-purpose approach. In this paper, such general approach is proposed, based on an extension of the SNN (Shared Nearest Neighbor) algorithm. The 4D+SNN algorithm allows the integration of space, time and one or more semantic attributes in the clustering process. This algorithm is able to deal with different data sets and different discovery purposes as the user has the ability to weight the importance of each dimension in the discovery process. The results obtained are very promising as show interesting findings on data and open the possibility of integration of several dimensions of analysis in the clustering process.

Modelling and implementing big data warehouses for decision support

In the era of Big Data, many NoSQL databases emerged for the storage and later processing of vast volumes of data, using data structures that can follow columnar, key-value, document or graph formats. For analytical contexts, requiring a Big Data Warehouse, Hive is used as the driving force, allowing the analysis of vast amounts of data. Data models in Hive are usually defined taking into consideration the queries that need to be answered. In this work, a set of rules is presented for the transformation of multidimensional data models into Hive tables, making available data at different levels of detail. These several levels are suited for answering different queries, depending on the analytical needs. After the identification of the Hive tables, this paper summarizes a demonstration case in which the implementation of a specific Big Data architecture shows how the evolution from a traditional Data Warehouse to a Big Data Warehouse is possible.