Vinícius Maran

Reactive, proactive, and extensible situation-awareness in ambient assisted living

The identification and prediction of situations in smart houses.An approach developed with the principles of reactive and proactive behavior.A case study presented with sensors and appliances.The proactivity and reasoning about uncertainty supported by probabilistic ontology. Considering that the world population is aging, health-support issues are in evidence, and many dangerous situations concerning users in their living environment may arise. This paper presents an approach to allow Ambient Assisted Living (AAL) systems to identify and predict situations that may endanger users in their living environment. Elderly people generally have difficulties to perform daily activities as using some electronic device, cooking, dressing among others. This paper offers an alternative to acquire comprehension of a former persons behavioral providing this knowledge when cognitive impairments will occur. This is a significant improvement to traditional expert systems applied to AAL as the handling of the knowledge base is adapted to the failing users behavior. We consider the user profile dynamics where new and different situations not initially planned in the development of the systems may occur. This approach was developed considering the principles of reactive and proactive behavior and the extensibility capacity of the system. Systems developed following the above-described principles can react to the current situations and anticipate proactively a dangerous situation eliminating it or controlling its impact. The approach is evaluated and verified by a case study where unwanted, dangerous situations were created and subsequently detected. The case study is based on a simulated smart house fulfilled with sensors and appliances. An application was build based on the developed approach, and it was capable of choosing the most appropriate actions to solve the unwanted situations. The main contributions of the present work are the identification and prediction of situations in smart houses, an approach developed with the principles of reactive and proactive behavior validated with a case study and supported by a probabilistic ontology.

Proactive Domain Data Querying based on Context Information in Ambient Assisted Living Environments

Ubiquitous computing defines a set of technologies to make computing omnipresent in real life environments. In the area of ambient assisted living, ubiquitous technologies have been used to improve the life quality and expectancy for elderly people. Recently, researches have shown that the use of context-awareness combined with proactive actions can cause systems to act more appropriately in assistance to the user. In this paper, we present a new persistent and proactive data retrieval model for ambient assisted living systems. This model provides an architecture that is able to integrate information that is gathered from the user environment and considers the current user context to act in a proactive manner. The model was implemented on a service inte-grated in a Situation as a Service middleware and it was applied in a case study for evaluation and validation.

UPCaD: A Methodology of Integration Between Ontology-Based Context-Awareness Modeling and Relational Domain Data

Context-awareness is a key feature for ubiquitous computing scenarios applications. Currently, technologies and methodologies have been proposed for the integration of context-awareness concepts in intelligent information systems to adapt them to the execution of services, user interfaces and data retrieval. Recent research proposed conceptual modeling alternatives to the integration of the domain modeling in RDBMS and context-awareness modeling. The research described using highly expressiveness ontologies. The present work describes the UPCaD (Unified Process for Integration between Context-Awareness and Domain) methodology, which is composed of formalisms and processes to guide the data integration considering RDBMS and context modeling. The methodology was evaluated in a virtual learning environment application. The evaluation shows the possibility to use a highly expressive context ontology to filter the relational data query and discusses the main contributions of the methodology compared with recent approaches.

A systematic mapping on adaptive recommender approaches for ubiquitous environments

Recommender systems were first conceived to provide suggestions of interesting items to users. The evolution of such systems provided an understanding that a recommender system is currently used to diverse objectives. One of the current challenges in the field is to have approaches of recommendation that go beyond accuracy metrics. Since it is a very recent interest of the community, this review, also characterized as an exploratory search, provides an overview of the techniques in the area that tries to look beyond accuracy. More specifically, one of the characteristics that would provide such evolution to these systems is the adaptation. This review is then performed to find the existence and characteristics of such approaches. Of the total 438 papers returned in the submission of the search string, 57 papers were analyzed after two filtering processes. The papers have shown that the area is little explored and one of the reasons is the challenge to validate non-accuracy characteristics in such approaches.

Semantic Integration between Context-awareness and Domain Data to Bring Personalized Queries to Legacy Relational Databases

Context-awareness is a key feature in ubiquitous middleware. Mainly, it is applied to adapt services and interfaces of applications that use ubiquitous features. The application of context information to personalize data queries is a recent topic in computing and still presents a large number of challenges. One of the main gaps evidenced by this research field is the lack of integration between context information, which is designed and used by ubiquitous middleware, and domain data, which are frequently persisted in relational databases. This integration is necessary because context can be used as a filter for content query. This position paper presents a motivational scenario that clarifies the necessity of the integration between context, used by ubiquitous middleware, and relational data, a comparison between the state of the art of the field, a list of research opportunities in the field, and a proposal of a framework that uses ontologies to integrate context and domain data, modeled and stored in relational databases.

Situation awareness in the speech therapy domain: A systematic mapping study

Abstract Situation Awareness (SA) involves the correct interpretation of a situation, allowing a system to respond to the observed environment and providing support for decision making in many systems domains. Speech therapy is an example of domain where situation awareness can provide benefits, since practitioners should monitor the patient in order to perform therapeutic actions. However, there are a few studies in the area that address reasoning about a situation to improve these tasks. For this reason, this systematic mapping study aims to identify and compare different proposals in the speech therapy domain in order to verify which aspects related to obtaining and maintaining SA are supported. Our analyzes provide useful insights on whats aspects of SA are best integrated in the speech domain - such as knowledge bases and adaptation - and other aspects that remain to be improved - like the action support and projection abilities. Also, this paper includes statistics, methodologies used by different authors and other issues involving research processes. As main contributions, this work presents an overview of the SA integration in the speech therapy domain, discussing challenges in the area and providing directions for further research.

A Case-Based System Architecture based on Situation-Awareness for Speech Therapy.

Situation Awareness (SA) involves the correct interpretation of situations, allowing a system to respond to the observed environment and providing support for decision making in many systems domains. Speech therapy is an example of domain where situation awareness can provide benefits, since practitioners should monitor the patient in order to perform therapeutic actions. However, there are few proposals in the area that address reasoning about a situation to improve these tasks. Likewise, the case-based reasoning methodology is little approached, since existing proposals rarely use previous knowledge for problem solving. For this reason, this paper proposes a case-based architecture to assist Speech-Language Pathologists (SLPs) in tasks involving screening and diagnosis of speech sound disorders. We present the modules that compose the system’s architecture and results obtained from the evaluation using the Google Cloud Speech API. As main contributions, we present the architecture of a system that aims to be situation-aware, encompassing perception, comprehension and projection of actions in the environment. Also, we present and discuss the results, towards a speech therapy system for decision making support.

SQL Query Performance on Hadoop: An Analysis Focused on Large Databases of Brazilian Electronic Invoices.

Hadoop clusters have established themselves as a foundation for various applications and experiments in the field of high-performance processing of large datasets. In this context, SQL-on-Hadoop emerged as trend that combines the popularity of SQL with the performance of Hadoop. In this work, we analyze the performance of SQL queries on Hadoop, using the Impala engine, comparing it with a RDBMS-based approach. The analysis focuses on a large set of electronic invoice data, representing an important application to support fiscal audit operations. The experiments performed included frequent queries in this context, which were implemented with and without data partitioning in both RDBMS and Impala/Hadoop. The results show speedups from 2.7 to 14x with Impala/Hadoop for the queries considered, on a lower cost hardware/software platform.

Domain content querying using ontology-based context-awareness in information systems

Abstract Ubiquitous computing technologies have been applied in several areas. However, it still presents a number of challenges, both for the full implementation of technologies and for the integration with existing information systems. One of the main mismatches evidenced by recent works is how context-awareness, a widely used capability in ubiquitous computing and actual information systems with relational databases may be integrated to allow ubiquitous and traditional systems to query relational data sources without the necessity to modify the schema of the database. This paper presents an integration model relating context and domain information allowing relational data to be retrieved in context without the necessity to change the originally used relational queries. A set of linking rules and algorithms are formalized in a model and this model is implemented in a prototype. The evaluation of the model is performed by applying it in a case study in a Massive Open Online Course (MOOC) platform. The evaluation of the model by the application of it in a case study in a MOOC platform demonstrated the possibility to use an ontology frequently used in ubiquitous middleware as an extra filtering layer for information systems without the necessity to recreate queries or make a re-engineering in the relational database schema. The results of the queries after the application of the model showed an average decrease of 21% in returned tuples, which was evaluated as a significant reduce in tuple results.

Fuzzy Based Model to Detect Patient's Health Decline in Ambient Assisted Living.

Detecting a decline in the health condition of a patient may still be considered a challenge in Ambient Assisted Living (AAL) since the concept of ‘decline’ is vague and imprecise. In this context, Fuzzy Logic comes as an excellent alternative for AAL systems. This paper presents a model based on Fuzzy logic reasoning in order to identify a possible decline in the patient health condition. In order to achieve this goal, the model considers relevant situations that may somehow impact the patient. To evaluate the model, a case study was developed, showing that the developed model can simulate the human reasoning and be used in