Semantic data containers for realizing the full potential of system wide information management

Neumayr, Bernd; Gringinger, Eduard; Schuetz, Christoph G.; Schrefl, Michael; Wilson, Scott; Vennesland, Audun

doi:10.1109/dasc.2017.8102002

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…The result of DNOTAM filtering and prioritization may be packaged into a semantic container [61]. A semantic container collects data items into a package that comprises all the data items satisfying a certain membership condition, formulated in terms of an ATM domain ontology.…”

Section: Use Case 1: Pilot Briefingsmentioning

confidence: 99%

“…In KG-OLAP, merge and abstraction may also be conducted repeatedly and in an arbitrary order. We illustrate KG-OLAP following use cases of (contextualized) knowledge graphs for air traffic management (ATM) [41,71]; we draw from experience in collaborative research projects on the use of semantic technologies in ATM (see [34,61,79]). ATM KGs potentially comprise a wide variety of topics: events, weather, flight plans, infrastructure, equipment, orga-nizations, companies, and personnel.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge Graph OLAP

Schuetz

Bozzato

Neumayr

et al. 2021

Self Cite

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A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.

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Section: Use Case 1: Pilot Briefingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knowledge Graph OLAP

Schuetz

Bozzato

Neumayr

et al. 2021

Self Cite

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“…One of the approaches used is Semantic container which able not only to reduce the time consumption but also use the full potential of the SWIM which is interoperability in data sharing. Semantic container consists of set of data items (NOTAM, METAR), membership condition and administrative metadata [17]. The usage of data that is gathered from SWIM can be used to show flight mode changes at the waypoints to track and predict the flight mode transitions in real time for better air navigation system structure [18].…”

Section: Swim Concept For Flight Data Sharing Interoperabilitymentioning

confidence: 99%

Development and Analysis of 2d Flight Planning Search Engine Considering Fusion of Swim Data

Sentoso¹,

Ruseno²

2021

Angkasa

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Flight planning is one of the essential factors of the airline operation. The selection of routes will determine the economic value of the flight. However, some conditions may prevent the flight to use the most optimum route due to airspace restriction or weather condition. The research aims to develop a search engine program that uses dynamic flight parameters that considers fusion of System Wide Information Management (SWIM) data including weather data and NOTAM to produce the most optimum route in 2D flight planning. The Dijkstra’s pathfinding is implemented in Python programming language to produce the flight plan. The navigation data used is enroute airway in Indonesian FIR regions. The scenario used is a flight from Jakarta to Makassar with duration of 2 hours flight with considering the effect of restricted airspace and weather blockage during in-flight. The study also uses the optimum route produced by the algorithm to be compared with the possible alternate routes to define how optimum the route is. Adding a restricted airspace parameter will result in a new optimum flight plan that able avoids the airspace and the most minimum distance. The effect of external wind parameter could influence the optimum route which may vary depends on the speed of the wind.

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“…The semantic container approach as developed in the course of the BEST project * is a flexible way of compartmentalising ATM information that complements the service-oriented architecture of SWIM (System Wide Information Management) with techniques for ontology-based data description and discovery (8,12) . A semantic data container is a data product that consists of content and description.…”

Section: Introductionmentioning

confidence: 99%

Semantics-based summarisation of ATM information

Schuetz

Neumayr²,

Schrefl³

et al. 2019

Aeronaut. j.

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Pilot briefings, in their traditional form, drown pilots in a sea of information. Rather than unfocused swathes of air traffic management (ATM) information, pilots require only the information for their specific flight, preferably with an emphasis on the most important information. In this paper, we introduce the notion of ATM information cubes – in analogy to the well-established concept of Online analytical processing (OLAP) cubes in data warehousing. We propose a framework with merge and abstraction operations for the combination and summarization of the information in ATM information cubes to obtain management summaries of relevant information. To this end, we adopt the concept of semantic data container – a package of data items with a semantic description of the contents. The semantic descriptions then serve to hierarchically organise semantic containers along the dimensions of an ATM information cube. Leveraging this hierarchical organisation, a merge operation combines ATM information from individual semantic containers and collects the data items into composite containers. An abstraction operation summarises the data items within a semantic container, replacing individual data items with more abstract data items with summary information.

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Semantic data containers for realizing the full potential of system wide information management

Cited by 11 publications

References 6 publications

Knowledge Graph OLAP

Knowledge Graph OLAP

Development and Analysis of 2d Flight Planning Search Engine Considering Fusion of Swim Data

Semantics-based summarisation of ATM information

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