Intelligent Security

Intelligent Audit/Security

Statistics indicate that 80% of useful big data is geospatial big data related to a location; as a result, a location-based big data analysis is very important. Saltlux provides big data convergence analysis solutions which can interwork GIS and semantic geospatial modeling and supports in-depth analyses such as early detection of abnormalities, real-time marketing analyses, and analyses of reputation and local issues through connections with social big data.

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  • Customer Problems

Implementing sustainable operating systems, technology, and systems to converge and analyze various types of big data based on region and space.

  • Introduction Effects

Transportation optimization, Monitoring and improving environment, Analyses of local citizens’ voice and improvement of administrative services, Response to disaster and early detection of risks, Local marketing optimization, Strengthening national security system, and others.

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  • Main Functions

Semantic geography, Space modeling, Geographical ontology configuration, Geospatial reasoning, Geo-SPARQL support, Various spatial query and reasoning, Analyses of local-based social big data, Local issues and detection of risks, and others.

  • Built-in Products

STORM, RAINBOW, and BigO Interworking

  • Major Customers

Ministry of National Defense, National Geographic Information Institute, EU Citi-sense, EU LarKC, and others.

Overview

Utilization of semantic geospatial information in applicable cases to provide new geographic information by reasoning and connecting various classes of data related to geography such as geographic features, geographic geometric features, and humanistic geographic features with location coordinates.

Geospatial Information

Geospatial information is related information such as statistics, demography, sociology, genetic epidemiology, biology, geomorphology, and others based on geographic information with location coordinates. Location coordinate is WGS84 coordinate information collected by using sensor information of mobile terminal equipment. WGS84 is expressed by latitude and longitude on the surface of the earth from the center of the earth.

Semantic Technology

Semantic technology is a technology through which people and machines can understand, share, and manipulate data. For this, data modeling is needed to add, change and mutually connect relationship between data contained in the data set shared among programs.

Data modeling systematizes concepts of data, and defines relationships between concepts and substances corresponding to those relationships. This is shown by RDF-based ontologies expressing resources on the web. Semantic storage systems save ontologies in RDF formats and save one triple form such as Subject, Predicate, and Object in a sentence unit.

To query data needed to be processed by programs in semantic storage systems, queries are conducted using a query language, SPARQL. Of query contents, variables in queries whose value is not specified according to the relationship between data expressed within ontologies can inquire about the variable value through reasoning by a reasoning system.

Inference processing consists of Axiom-based reasoning, rules according to definitions of relationships between superior-subordinate of concepts and properties, and rule-based reasoning to find value conforming to the rules by defining concepts and properties using a rule language, SWRL. In addition, there are forward chaining reasoning systems that reason using relationships between data in advance and backward chaining reasoning that reason when querying as data frequently changes.

Application Cases

Saltlux has established the Seoul Traffic Sign Management System, which shows inference processing results for data processing and application processing, and conducts ontology modeling based on Geographical Information Open Street Map (www.openstreetmap.org), Point of Interest (POI) in Seoul area and Korea Institute of Civil Engineering and Building Technology’s Traffic Signs in Seoul.

  • POI, traffic signs, and road-waypoints are the superordinate concepts with WGS84 coordinates, which have a node.
  • POI, traffic signs, and road-waypoints are the superordinate concepts with WGS84 coordinates, which have a node.
  • A way is the connection between links. Ways have the opposite direction each other.
  • Road information is conceptualized using relations between road-waypoints.
  • After establishing concepts, a relationship between concepts is established for reasoning.
  • Roads are ways that have the opposite direction of each other.
  • A point where roads join is a junction, where traffic can change direction.
  • It can be displayed by triple such as < Roadsigns > < indicate > < POI >.
  • If subdividing ‘Indicate’ in more detail, it indicates the right direction, indicates the straight-ahead direction, and indicates the left direction.
  • Ontology modeling expresses road information conceptually, with traffic signs and their contents on the Open Street Map.
  • A link with two road-waypoints as a starting point and an ending point is the minimum unit of road information.
  • The data set of POI in Seoul area consists of WGS84 and Korean names.
  • Road information and POI on the Open Street Map have Korean and English names based on the WGS84 geographic coordinate system. For example, in the case of “삼성역” as POI, it shows “삼성역” and “Samsung Subway Station”. Road information shows “올림픽대로” and “Olympic Highway”.
  • Traffic signs are specified as POI such as special place names, intersections, and administrative region information. The relationship between traffic signs and POI is such that traffic signs indicate the POI direction.

The cases to apply reasoning to the traffic sign management system are as follows:

  • The end point of a link that ends finally becomes a junction and the junction’s representative waypoint is found using owl:sameAs relations. The junction waypoint, which becomes the starting point of the next link, and the starting point of the next link that changes direction will be found by reasoning owl:sameAs links and representative waypoints.
  • Another reasoning element is the reasoning of the relationship: in the case of the query in which the traffic sign indicating the Samsung Subway Station direction is among traffic signs, the traffic sign indicating Samsung Subway Station is found by rdf:subPropertyOf relations among the right, the straight-ahead, and the left direction, or sub properties of ‘indicate’.
  • A junction where roads join should share one road-waypoint. The Open Street Map is an open map whose data is measured with a sensor of terminal equipment and stored by various people. For this reason, road-waypoints of a junction can be displayed differently from each other.
  • If traffic sign 1 indicates the left direction from Samsung Subway Station, as it means the traffic sign 1 indicates the left direction, the traffic sign 1 is found.
  • To make different road-waypoints which are a junction become the semantically same point, it defines that different road-waypoints are identical to road-waypoints representing junctions by using owl:sameAs property among ontological relationship expressions.
  • In inspecting the validity of traffic signs, if looking for POI by following a link in the direction indicated by the traffic sign to find certain POI displayed on traffic signs, it is needed to change the direction at a junction. To change the direction, the following link should be the link whose direction has changed. As the starting point of the following link becomes a junction, people do not know which road-waypoint among a junction’s many road-waypoints should be the starting point.