Entity-Attribute-Value Model Research Articles

Machine Vision in Autonomous Vehicles: Designing and Testing the Decision Making Algorithm Based on Entity Attribute Value Model

If we speak about the Smart City’s transport system, autonomous vehicles idea is the first thing that comes to mind. Today, it is strongly believed that the autonomous vehicles’ introduction into the traffic will increase the road safety. However, driverless cars are not the solution by itself. The road safety and, accordingly, sustainability will strongly depend on decision making algorithms inbuilt into the control module. Therefore, the goal of our research is to design and test the data mining algorithm based on Entity–Attribute–Value (EAV) model for decision making in the Intelligent System in the fully- or semi-autonomous vehicles. In this article, we describe the methodology to create 3 main modules of the designed Intelligent System: (1) an Object detection module; (2) a Data analysis module; (3) a Knowledge database built on decision rules generated with the help of our data mining algorithm. To build the Decision Table on the base of the real data, we have tested our algorithm on a simple collection of photos from a Polish two-lane road. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, our decision making algorithm thanks to excluding the mistakes made on the object detection stage, works faster than existing ones with the same level of correctness.

Decision Rules Construction: Algorithm Based on EAV Model.

In the paper, an approach for decision rules construction is proposed. It is studied from the point of view of the supervised machine learning task, i.e., classification, and from the point of view of knowledge representation. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, the proposed algorithm is based on transformation of decision table into entity–attribute–value (EAV) format. Additionally, standard deviation function for computation of averages’ values of attributes in particular decision classes was introduced. It allows to select from the whole set of attributes only these which provide the highest degree of information about the decision. Construction of decision rules is performed based on idea of partitioning of a decision table into corresponding subtables. In opposite to dynamic programming approach, not all attributes need to be taken into account but only these with the highest values of standard deviation per decision classes. Consequently, the proposed solution is more time efficient because of lower computational complexity. In the framework of experimental results, support and length of decision rules were computed and compared with the values of optimal rules. The classification error for data sets from UCI Machine Learning Repository was also obtained and compared with the ones for dynamic programming approach. Performed experiments show that constructed rules are not far from the optimal ones and classification results are comparable to these obtained in the framework of the dynamic programming extension.

Anomaly free search using multi table entity attribute value data model

In this paper, the authors are proposing a principled extension of dynamic tables (DT). It is termed multi table entity attribute value (MTEAV) model which offers a search efficient avenue for storing database. The authors present precise semantics of MTEAV and demonstrate the following: 1) MTEAV possesses consistency and availability; 2) MTEAV outperforms other existing models (entity attribute value model, dynamic tables, optimised entity attribute value and optimised column oriented model) under various query scenarios and varying datasets size; 3) MTEAV retains flavour of EAV in terms of handling sparseness and self-adapting schema changing capability. To heighten adaptability of MTEAV, authors implement a translation layer over existing SQL engine in a non-intrusive way. Translation layer transforms conventional SQL query (as per horizontal row representation) to a new SQL query (as per MTEAV structure) to maintain user friendliness. Translation layer makes user feel as if he/she is interacting with the conventional horizontal row approach. The authors also critically analyse maximum percentage of non-null density appropriate for choosing MTEAV as a storage option.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

BackgroundSocial networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics.ObjectiveThe aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis.MethodsThe domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts.ResultsWe developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, “academic stresses” and “suicide” contributed negatively to the sentiment of adolescent depression.ConclusionsThe ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.

Database of Physical Protection of Potentially Hazardous Objects

Эффективность построения системы физической защиты потенциально опасных объектов во многом зависит от качественного подбора комплектующих. Ввиду того что на сегодняшний день не существует единой базы технических средств физической защиты, предлагается разработанная с помощью MS SQL Server Express база данных «Средства физической защиты потенциально опасных объектов». Для проектирования базы данных был выбран Entity-Attribute-Value (EAV) - паттерн. Особенностью EAV-подхода к проектированию является представление данных в виде разреженной матрицы, где сохраняются только непустые значения. Представлена концептуальная модель предметной области с учетом особенностей EAV-подхода проектирования. В отличие от реляционной модели данных, где каждая сущность хранится в виде отдельной таблицы в базе данных, в модели EAV каждый экземпляр сущности - это объект. База данных «Средства физической защиты потенциально опасных объектов» согласно ER-диаграмме состоит из таблиц: «objtype», «objects», «strdesc», «propdesc», «strings», «properties», «units», что позволяет хранить информацию о множестве разнообразных средств физической защиты с различными атрибутами.

Development and evaluation of data entry templates based on the entity-attribute-value model for clinical decision support of pressure ulcer wound management

PurposesThe purpose of this study was to develop and evaluate the functionality of structured data entry templates using the entity-attribute-value (EAV) model for clinical decision support of pressure ulcer wound management. MethodsA data set for wound assessment of pressure ulcers that has commonly been recommended by clinical practice guidelines was identified, and then the EAV models on each data were developed. Structured data entry templates and a database were developed based on these EAV models. These were integrated with a knowledge engine into the clinical decision support system (CDSS) to provide patient-specific recommendations on pressure ulcer wound management. The functionality of the EAV model and structured data entry templates for the CDSS was evaluated heuristically by five nurse experts using clinical scenarios. ResultsThe data set containing 13 entities was identified and EAV models of these entities were created. Cardinalities and data types of attributes were defined to represent the models in more detail. Terms used in the EAV models were mapped to SNOMED CT concepts. Six data entry templates and the relational database with ten tables were developed. Five nurses successfully entered all data in the scenarios except one data element and retrieved expected recommendations successfully from the clinical decision support system when all data were entered correctly. ConclusionsThe clinical data models and structured data entry templates developed in this study were useful in supporting clinical decision making on pressure ulcer wound management.

A Dimensional Bus model for integrating clinical and research data.

Many clinical research data integration platforms rely on the Entity-Attribute-Value model because of its flexibility, even though it presents problems in query formulation and execution time. The authors sought more balance in these traits. Borrowing concepts from Entity-Attribute-Value and from enterprise data warehousing, the authors designed an alternative called the Dimensional Bus model and used it to integrate electronic medical record, sponsored study, and biorepository data. Each type of observational collection has its own table, and the structure of these tables varies to suit the source data. The observational tables are linked to the Bus, which holds provenance information and links to various classificatory dimensions that amplify the meaning of the data or facilitate its query and exposure management. The authors implemented a Bus-based clinical research data repository with a query system that flexibly manages data access and confidentiality, facilitates catalog search, and readily formulates and compiles complex queries. The design provides a workable way to manage and query mixed schemas in a data warehouse.

Search Efficient Representation of Healthcare Data based on the HL7 RIM

The Reference Information Model (RIM) of Health Level Seven (HL7) standard is a conceptual information model for health care. Data of HL7 RIM Observation class are sparse, high dimensional, and require frequent schema change. Entity Attribute Value (EAV) is the widely used solution to handle these above challenges of medical data, but EAV is not search efficient for knowledge extraction. In this paper, we have proposed a search efficient data model: Optimized Entity Attribute Value (OEAV) for physical representation of medical data as alternative of widely used EAV model. We have implemented EAV or OEAV individually to model RIM Observation class and used relational model for the remaining RIM classes. We have shown that OEAV is dramatically search efficient and occupy less storage space compared to EAV.

PhD: a web database application for phenotype data management

A database application has been developed for phenotype data management employing the Entity-Attribute-Value (EAV) model. By applying the EAV model, this application allows users to manage arbitrary phenotypes and customize data entry forms; therefore, it is suitable for different and multi-center projects.