Enriching integrated statistical open city data by combining equational knowledge and missing value imputation

Bischof, Stefan; Harth, Andreas; Kämpgen, Benedikt; Polleres, Axel; Schneider, Patrik

doi:10.1016/j.websem.2017.09.003

Cited by 19 publications

(13 citation statements)

References 47 publications

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“…Using self-categorized information [85] as facets is another. Attempts to better represent the underlying data [22] do have an affect on search. This includes better links with others data [43].…”

Section: Data Handlingmentioning

confidence: 99%

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Dataset search: a survey

et al. 2019

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Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

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Section: Data Handlingmentioning

confidence: 99%

“…Given that a dataset is by definition a collection of pieces, imputation of missing pieces needs greater scrutiny. As discussed in Section 4, imputation efforts are underway [6,22,92,126] but draw heav-ily from web techniques. The imputation methods from the data management community should be considered.…”

Section: Database Building Blocksmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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“…An ontology framework was developed to support the system, along with the Linked Data method used to integrate heterogeneous information from multiple sources including government, transport operators, and the public. Especially, the Open City Data Pipeline was presented by [74]. The framework, which is a platform for collecting, integrating, and enriching open city data from several data providers, contains a data crawler, ontology-based integration platform, and missing value prediction module.…”

Section: B Linked Open Data For Tourismmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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“…Their performances vary based on several parameters, such as the type of the targeted data: Categorical, numerical, or mixed [19], the percentage of missing data [20] or the application domain of the completion task, such as biology [21] or pattern recognition [22]. MVI has been carried out using statistical techniques such as simple means, Multiple Linear Regressions (MLR), Logistic Regressions (LR), Random Forest Decision Trees (RFD), or Bayesian inference [10,[23][24][25][26][27]. It now benefits from the most recent developments in Machine Learning techniques such as K-Nearest Neighbour (KNN), Support Vector Machines, Artificial Neural Networks, Long Short-Term Memory algorithms [20,[28][29][30][31], and more recently Graph Neural Networks [32].…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Bel-Ghaddar

Chahinian

Seriai³

et al. 2021

Water

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Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion.

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Enriching integrated statistical open city data by combining equational knowledge and missing value imputation

Cited by 19 publications

References 47 publications

Dataset search: a survey

Dataset search: a survey

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

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