Applying Semantics to Reduce the Time to Analytics within Complex Heterogeneous Infrastructures

Pomp, André; Paulus, Alexander; Kirmse, Andreas; Kraus, Vadim; Meisen, Tobias

doi:10.3390/technologies6030086

Cited by 27 publications

(17 citation statements)

References 13 publications

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“…Based on this UI, users choose concepts and relations from ESKAPE's underlying knowledge graph, which encourages them to build choices upon that shared terminology. Compared to traditional OBDM approaches, ESKAPE's knowledge graph, which Pomp et al dene in [36] and whose implementation details are described in [35], allows users to extend its vocabulary by introducing new concepts or relations on-demand directly to the knowledge graph to make them available for others. While this previous work is a good rst step for managing a knowledge graph and extending it on-demand, its implementation still had limitations.…”

Section: Motivating Examplementioning

confidence: 99%

“…In order to overcome the issues of conventional ontologies used in OBDM, we propose an approach featuring an evolutionary knowledge graph that consists of an internal growing ontology (universal knowledge) and data source specic mappings (local knowledge) (cf. knowledge graph denition given in [36]). These two building blocks together form a domain conceptualization, serve as a data source index and are used to continuously adapt and extend the knowledge graph on-demand.…”

Section: Introductionmentioning

confidence: 99%

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Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Pomp

Lipp

Meisen

2019

IJRC

Self Cite

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Companies suer from heterogeneous data sources that are distributed across all business units and locations. Accessing, discovering, and understanding these data sources is challenging because data scientists have to deal with dierent protocols, data formats, and even company-specic organizational issues such as rewalls and privacy policies. Ontology-Based Data Management (OBDM) provides dierent aspects for reducing the barriers of integrating, accessing and managing heterogeneous data sources by using ontologies. For that, we establish a mapping between data sources and one or multiple target ontologies. However, the biggest challenges in ODBM are creation and administration of required ontologies. Usually, these ontologies are created in advance, for instance, by ontology engineers and domain experts that closely work together for manually designing and even maintaining the ontology. In order to enhance the process of designing and maintaining an ontology, we propose an approach consisting of an evolving knowledge graph that includes an internal ontology, which continuously evolves on demand as domain experts add new data sources and dene the mapping between the ontology and that data source. For this purpose, we develop an intuitive, user-oriented wizard and combine it with a semi-supervised evolution strategy that supports the user with the help of external knowledge databases. Moreover, we equip the system with additional logic that allows to automatically link related concepts of the ontology. We evaluate the accuracy and usability of our approach by conducting a user 20 André Pomp et al. study. The results show that mappings become more objective and consistent with our provided user wizard, resulting in a knowledge graph with higher connectivity and stability.

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Section: Motivating Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Pomp

Lipp

Meisen

2019

IJRC

Self Cite

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“…Statistical institutes that wish to open their data must deal with the intrinsic complexity of their data structures. To address this, we could define an ontology as proposed in [16] or [17]. In this paper, we propose a different approach, defining a methodology based on the R language (see [18] or [19]), that simplifies the CRUD (create, read, update and delete) operations that can be performed on the data.…”

Section: The Proposed Solutionmentioning

confidence: 99%

Using the R Language to Manage and Show Statistical Information in the Cloud

Casas

Tormos

2018

Technologies

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We present a methodology to enable users to interact with the statistical information (survey data) of a public opinion institute which is stored in Cloud infrastructure. Mainly using R, this approach was developed following the open-data philosophy. Also, as we used R, the implementation is mainly based on open-source software. R has several advantages from the point of view of data management and acquisition, as it is becoming a common framework that can be used to structure the processes involved in any statistical operation. It further simplifies the access to data and enables the use of all the power of R in Cloud infrastructure. This methodology was applied successfully to develop a tool to manage the data of the Centre d'Estudis d'Opinió, but it can be applied to other institutions to enable open access to their data. The infrastructure was also deployed to Cloud infrastructure to assure its scalability and 24/7 access. improve access to Federal data and expand creative use of those data beyond the walls of government by encouraging innovative ideas (e.g., web applications)".There are several websites and tools to access public statistical information as well as several applications that allow the reuse and sharing of code related to the access of public information, such as [8] or [9]. The next step is to allow users without technical knowledge to access the information and perform easy statistical tasks with it. To do this, the user must be able to execute those tasks on a remote server that stores both remote information and certain statistical functions.The possibilities of allowing end-users to execute certain statistical functions to obtain new information from the data were described by [10]. Currently, there are different tools that to show information over the web and allow the execution of statistical functions by the end users, e.g., the NESSTAR system [11]. In parallel with these proprietary solutions, several efforts are focused on developing APIs (Application Protocol Interfaces) to access statistical information. As an example, Data.org is preparing an API that allows users to interact with the system data to build their own applications and mash-ups; in [12], an API has also been implemented to interact with its data. However, the question of how to develop and use these APIs remains open. Every infrastructure that develops this type of solution implements a new API, and the developers must be able to address all of them.Another problem is related to the data preparation; there are different alternatives already available to define the surveys, e.g., [13] or [14]. These tools allow the user to export the data to various formats and perform posterior analyses (a well-known format is the Triple-S, an XML for survey software that enables the user to import and export surveys between different software). The main issue with this approach is that manual operations are required to process the data. In our proposed approximation, once the surveys are completed, their datasets can easily be upload...

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“…Statistical institutes that wish to open his data have to deal with intrinsic complexity of their data structures. To arrange these, we could define an ontology as proposed on [16] or [17]. On this paper we propose a different approach, defining a methodology based on the R language see [18] or [19] that simplifies the CRUD (create, read, update and delete) operations that can be performed over the data.…”

Section: The Proposed Solutionmentioning

confidence: 99%

Using the R Language to Manage and Show Statistical Information in the Cloud

Casas¹,

Tormos²

2018

Preprint

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We present a methodology to enable users to interact with statistical information owned by an institution and stored in a cloud infrastructure. Mainly based on R, this approach was developed following the open-data philosophy. Also, since we use R, the implementation is mainly based on open-source software. R gives several advantages from the point of view of data management and acquisition, as it becomes a common framework that can be used to structure the processes involved in any statistical operation. This simplifies the access to the data and enable to use all the power of R in the cloud information. This methodology was applied successfully to develop a tool to manage the data of the Centre d’Estudis d’Opinió, but it can be applied to other institutions to enable open access to its data. The infrastructure also was deployed to a cloud infrastructure, to assure the scalability and a 24/7 access.

show abstract

Applying Semantics to Reduce the Time to Analytics within Complex Heterogeneous Infrastructures

Cited by 27 publications

References 13 publications

Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Using the R Language to Manage and Show Statistical Information in the Cloud

Using the R Language to Manage and Show Statistical Information in the Cloud

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