J. Intell. Syst. 2017; 26(2): 197–213 Sunitha Abburu* and Nitant Dube Ontology Concept-Based Management and Semantic Retrieval of Satellite Data DOI 10.1515/jisys-2015-0082 Received July 26, 2015; previously published online February 25, 2016. Abstract: Several satellite data receiving and distributing centers across the world support data storage, processing, and retrieval based on satellite, sensor, product, latitude, longitude, date and time, etc. These systems address queries on satellite products that are mostly high-level concepts. A more sophisticated retrieval system that supports ontological concepts, subconcepts, and concept hierarchical queries delivers refined results that broaden the scientific horizon of the application domain. To achieve this, the current research designed and implemented an ontology concept-based satellite data management and retrieval methodology. This enhances the performance of the satellite data retrieval system and supports semantic queries. The performance of the retrieval system depends upon the strategy followed to maintain domain ontologies and satellite data instances. Three ontology-based satellite data management strategies are dis- cussed, and their performance was evaluated by taking real and benchmark metrics. A semantic query set of 25 queries was chosen covering various concepts, subconcepts, and concept hierarchical-related queries that involve various SPARQL query constructs. The test bed is taken from real-time satellite data received from Kalpana-1 of various sizes of triple stores. Keywords: Satellite data, ontology, semantic retrieval, visualization. Dedication: Dedicated to Space Applications Centre (SAC) RESPOND, Indian Space Research Organization (ISRO), Govt. of India. 1 Introduction Satellite data centers store, classify, and retrieve satellite data based on satellite, sensor, date, time, lati- tude, longitude, etc. In addition to this, application domain-specific concept-based data retrieval facilitates more domain-specific data. Domain-specific concept-based retrieval requires a semantic concept-based sat- ellite data storage process. The concept-based retrieval system broadens the horizon of scientific application research and enhance interdisciplinary research. An effective concept-based satellite data retrieval can be achieved with the support of semantic technology. Semantic technology is a powerful technology to represent domain knowledge in machine-understanda- ble format [5]. For knowledge representation, semantic technology uses a powerful technique called ontology. Ontology is an explicit formal specification of a shared conceptualization [7]. Ontology provides concept defi- nitions, hierarchies, and relationship between concepts of a domain. Ontologies enhance the performance of current information retrieval systems. Ontology provides solutions for the issues of modern information systems [15, 18, 38, 40]. Ontology-based semantic representation of satellite data enables semantic concept-based satellite data processing and retrieval. Remote sensing technology provides data pertaining to several fields such as Earth observation (EO), environment, marine, meteorology, etc. Semantic representation of satellite data requires *Corresponding author: Sunitha Abburu, Professor and Director, Department of Computer Applications, Adhiyamaan College of Engineering, Tamil Nadu, India, e-mail: [email protected] Nitant Dube: MSDPD/DPSG/SIPA, Space Applications Centre, ISRO, Ahmedabad, India 198 S. Abburu and N. Dube: Ontology Based Satellite Data Management and Retrieval multiple ontologies of various application domains. The effectiveness of the semantic retrieval system depends on ontology-based knowledge management structure. There is a need for an effective multiple ontol- ogy-based satellite data management methodology that facilitates improved semantic retrieval of satellite data. The current research work describes an approach that supports ontology concept-based satellite data retrieval and three different strategies for ontology-based satellite data management. The three data manage- ment strategies are evaluated using popular real and benchmark metrics for SPARQL queries. The current work also provides a SPARQL query interface to execute ontology concept-based semantic queries on the satellite data and presents results on geographic maps. The rest of the paper is organized as follows. Section 2 gives a discussion on background work; Section 3 illustrates the methodology; Section 4 describes performance evaluation and semantic concept-based query results; and Section 5 concludes. 2 Related Research Work Satellite data play a vital role in many applications such as weather and forecasting, environmental monitor- ing, urban planning, etc. There are several satellite data receiving and distributing centers across the world. The following paragraphs describe a few popular satellite data centers and their retrieval system. The Indian National Centre for Ocean Information Services (INCOIS) is the central repository for marine data in India. It receives voluminous oceanographic data from a variety of in situ and remote sensing observing systems. INCOIS is one of the major marine data service providers. The INCOIS search interface [9] provides data based on product, sensor, satellite, and date. INCOIS also provides data through a Live Data Access (LDA) service [8]. INCOIS LDA service provides data based on dataset, latitude, longitude, and time. The Meteorological and Oceanographic Satellite Data Archival Center (MOSDAC) is an efficient data portal to satisfy the vast meteorological and oceanographic data needs of researchers. MOSDAC is a major meteorological and oceanographic data provider. MOSDAC provides two choices for satellite data access: metadata search [21] and advanced meta search [20]. The former searches satellite data based on the satellite, sensor, and parameter. Advanced search provides a keyword-based search for the satellite data. It performs search based on the satellite, sensor, parameter, processing level, frequency, resolution, temporal values, and keywords. The National Remote Sensing Centre (NRSC) is one of the centers of the Indian Space Research Organiza- tion (ISRO). The NRSC/ISRO Open data and product archive [4] facilitates users to select, browse, and down- load data from this portal. This portal search interface retrieves data based on satellite, product, latitude, and longitude. The National Oceanic and Atmospheric Administration [24] manages a constellation of geostationary and polar-orbiting meteorological spacecraft. It provides data through four data centers: (i) National Centers for Environmental Information (NCEI), (ii) National Oceanographic Data Center (NODC), (iii) National Climate Data Centre (NCDC), and (iv) National Geographical Data Centre. The NCEI provides quick links [23] to access data. The NODC search interface [25] provides data based on keyword, time, and content type. The NCDC retrieval system [22] provides data based on parameter, time scale, month, state, and city. The National Snow and Ice Data Center archives and distributes digital and analog snow and ice data. The data center provides data based on data item, time, latitude, and longitude [26]. The above satellite data center retrieval systems provide data based on satellite, sensor, parameter latitude, longitude, and time. Several research organizations and communities introduced ontologies in satellite remote sensing technology to improve the processing and utility of satellite data. Due to vast applications in diverse fields of remote sensing technology, several ontologies of remote sensing domain are developed by many space research organizations. S. Abburu and N. Dube: Ontology Based Satellite Data Management and Retrieval 199 The Semantic Web for Earth and Environmental Terminology (SWEET) [36] is a collection of ontologies developed by the National Aeronautics and Space Administration Jet Propulsion Laboratory. SWEET devel- oped 200 ontologies with 6000 concepts. SWEET covers a wide range of concepts and relations among the concepts in the domain of Earth and the environment [32]. Marine Metadata Interoperability [19] is a project funded by National Science Foundation. It provides several ontologies, vocabularies, and semantic services to achieve interoperability in marine data. Many researchers are using ontologies for semantic representation of satellite images and retrieval [2, 6, 14, 37]. Little work has been done toward ontology-based semantic processing of satellite gridded data, stored in scientific file formats. Jiapeng et al. [11] proposed an ontology-based production model of parameter products of satellite remote sensing data. The model describes the design of satellite ontology, remote sensing data ontology, and parameter products ontology. In this method, a production system architecture of land surface parameter products is designed based on the ontology model. Jingzun et al. [12] used ontologies for remote sensing quantitative retrieval. To achieve this, the approach extends the RS-SECI (Remote Sensing – Socialization, Externalization, Combination, and Internalization) model. The method uses four ontologies: general ontology, domain-specific ontology, geospatial data ontol- ogy, and geospatial process ontology. The ontologies are used for geospatial processing to represent knowl- edge of the remote sensing domain. The remote sensing quantitative retrieval model defines process flow and relation between processes in the retrieval