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Due to the intelligent, adaptive nature of various operations and their ability to provide maximum comfort to the occupants residing in them, smart buildings are becoming a pioneering area of research. Since these architectures leverage the Internet of Things (IoT), there is a need for monitoring different operations (occupancy, humidity etc.) to provide sustainable comfort to the occupants. This work proposes a novel approach for intelligent building operations monitoring using rule-based complex event processing (CEP) and query-based approaches for dynamically monitoring the operations. Siddhi is a CEP engine designed for handling multiple sources of events data in real time and processing it according to predefined rules using a decision tree. Since datastream is dynamic in nature, to keep track of different operations, we have converted the IoT data into an RDF (Resource Description Framework) dataset. The RDF dataset is ingested to Apache Kafka for streaming and for stored data we have used GraphDB tool that extracts information with the help of SPARQL query. Additionally, a large number of events are passed through CEP for correlating and analyzing events in real time, and we measure the performance of CEP engines through SPARQL for enhancing decision support. The proposed approach is evaluated by deploying rules to events through the CEP engine and assessing their processing efficiency in terms of time. Subsequently, a risk estimation scenario is proposed to generate alerts for end users if any smart building operations need immediate attention. However, the proposed work has limitations regarding adaptive and standard parameters, which may cause the rules to fail in adapting to changing data streams. [ABSTRACT FROM AUTHOR]

Vector-borne diseases (VBDs) are a kind of infection caused through the transmission of vectors generated by the bites of infected parasites, bacteria, and viruses, such as ticks, mosquitoes, triatomine bugs, blackflies, and sandflies. If these diseases are not properly treated within a reasonable time frame, the mortality rate may rise. In this work, we propose a set of ontologies that will help in the diagnosis and treatment of vector-borne diseases. For developing VBD's ontology, electronic health records taken from the Indian Health Records website, text data generated from Indian government medical mobile applications, and doctors' prescribed handwritten notes of patients are used as input. This data is then converted into correct text using Optical Character Recognition (OCR) and a spelling checker after pre-processing. Natural Language Processing (NLP) is applied for entity extraction from text data for making Resource Description Framework (RDF) medical data with the help of the Patient Clinical Data (PCD) ontology. Afterwards, Basic Formal Ontology (BFO), National Vector Borne Disease Control Program (NVBDCP) guidelines, and RDF medical data are used to develop ontologies for VBDs, and Semantic Web Rule Language (SWRL) rules are applied for diagnosis and treatment. The developed ontology helps in the construction of decision support systems (DSS) for the NVBDCP to control these diseases. [ABSTRACT FROM AUTHOR]