Paulo Carreira

Execution of data mappers

Data mappers are essential operators for implementing data transformations supporting schema mapping and integration scenarios such as legacy data migration, ETL processes for data warehousing, data cleaning activities, and business integration initiatives. Despite their widespread use, no formalization of this important operation has been proposed so far. In this paper we propose the data mapper operator as an extension to the relational algebra. We supply a set of algebraic rewriting rules for optimizing queries that combine standard relational operators with data mappers. Finally, we propose algorithms for their efficient physical execution.

Efficient development of data migration transformations

In this paper, we present a data migration tool named DATA FUSION. Its main features are: A domain specific language designed to conveniently model complex data transformations; an integrated development environment that assists users on managing complex data transformation projects and an auditing facility that provides relevant information to project managers and external auditors.

Towards Automatic Conflict Detection in Home and Building Automation Systems

Home and Building Automation Systems (HBAS) are becoming of widespread adoption. When distinct users interact with such systems, their intentions are likely to be different, often resulting in conflicting situations, which the systems ought to recognize and resolve automatically. This work aims at investigating conflict in HBAS and creating a solution to detect and resolve them. Herein, we review the literature concerning conflict detection and resolution, and propose a formal framework based on constraint solving that enables detecting and solving conflict situations automatically.

Conflict detection and resolution in home and building automation systems: a literature review

The evolution and increasing commoditization of home and building automation systems (HBAS) is contributing to their widespread adoption. However, an effort must still be made to render them usable, intelligent, highly adaptive and able to fulfill users’ needs. When distinct users interact with such a system, their intentions are likely to be different, often resulting in conflicting situations, which the system ought to recognize and, if possible, resolve automatically. However, conflict detection and resolution in HBAS are not yet fully understood. This work aims at investigating conflict in Ambient Intelligence systems, namely those supported by HBAS. Our main contribution is a systematization and review of existing literature concerning conflict detection and resolution in these systems.

Visual search light model for mental problem solving

This paper proposes a model of mental imagery which takes into account the role of internal attentional search light. We model the process of mental imagery problem solving by a long term memory which manipulates, with the aid of associations, the information of the visual buffer. The visual buffer is changed until a desired solution for a visual problem is achieved. The associations are determined in the retrieval phase of the long term memory. No variable binding mechanism is used. Instead, the search light model is applied to determine, which associations can be executed. The behavior of the model is demonstrated by empirical experiments in geometric block world.

One-to-many data transformations through data mappers

The optimization capabilities of RDBMSs make them attractive for executing data transformations. However, despite the fact that many useful data transformations can be expressed as relational queries, an important class of data transformations that produce several output tuples for a single input tuple cannot be expressed in that way. To overcome this limitation, we propose to extend Relational Algebra with a new operator named data mapper. In this paper, we formalize the data mapper operator and investigate some of its properties. We then propose a set of algebraic rewriting rules that enable the logical optimization of expressions with mappers and prove their correctness. Finally, we experimentally study the proposed optimizations and identify the key factors that influence the optimization gains.

A Learning Approach for Energy Efficiency Optimization by Occupancy Detection

Building Automation Systems control HVAC systems aiming at optimizing energy efficiency and comfort. However, these systems use pre-set configurations, which usually do not correspond to occupants’ preferences. Although existing systems take into account the number of occupants and the energy consumption, individual occupant preferences are disregarded. Indeed, there is no way for occupants to specify their preferences to HVAC system. This paper proposes an innovation in the management of HVAC systems: a system that tracks the occupants preferences, and manages automatically the ventilation and heating levels accordingly to their preferences, allowing the system to pool its resources to saving energy while maintaining user comfort levels. A prototype solution implementation is described and evaluated by simulation using occupants’ votes. Our findings indicate that one of the algorithms is able to successfully maintain the appropriate comfort levels while also reducing energy consumption by comparing with a standard scenario.

Data mapper: an operator for expressing one-to-many data transformations

Transforming data is a fundamental operation in application scenarios involving data integration, legacy data migration, data cleaning, and extract-transform-load processes. Data transformations are often implemented as relational queries that aim at leveraging the optimization capabilities of most RDBMSs. However, relational query languages like SQL are not expressive enough to specify an important class of data transformations that produce several output tuples for a single input tuple. This class of data transformations is required for solving the data heterogeneities that occur when source data represents an aggregation of target data. n nIn this paper, we propose and formally define the data mapper operator as an extension of the relational algebra to address one-to-many data transformations. We supply an algebraic rewriting technique that enables the optimization of data transformation expressions that combine filters expressed as standard relational operators with mappers. Furthermore, we identify the two main factors that influence the expected optimization gains.

On Handling One-to-Many Transformations in Relational Systems

The optimization capabilities of RDBMSs make them attractive for executing data transformations that support ETL, data cleaning and integration activities. Despite the fact that many useful data transformations can be expressed as relational queries, an important class of data transformations that produces several output tuples for a single input tuple are not adequately supported by RDBMSs.