Fuzzy Similarity and Fuzzy Inclusion Measures in Polyline Matching: A Case Study of Potential Streams Identification for Archaeological Modelling in GIS

Ďuračiová, Renata; Rášová, Alexandra; Lieskovský, Tibor

doi:10.1515/rgg-2017-0020

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…After balancing, the MFs shapes are redefined using trapezoidal (eq. 3), L-shaped [13] and Γ-shaped [15] MFs. Fuzzy Inference applies a set of fuzzy rules and a particular fuzzy inference approach, like Mamdani or Takagi-Sugeno (T-S), to fuzzy inputs for generating a fuzzy output.…”

Section: Fuzzy Inference Controller (Fuzzic)mentioning

confidence: 99%

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An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

Miliauskaitė

Kalibatiene

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Today, various stakeholders provide a large number of functionally similarWeb Services (WS) to meet increasingly complex business needs. Therefore, to distinguish similar WS, some researchers have proposed using the non-functional characteristics, named Quality of Service (QoS), and user’s needs, expressed through Quality of Experience (QoE). Thus, all those QoS and QoE attributes should be taken into account in predicting WS quality jointly, called WS QoSE (Quality of Service and Experience). However, these attributes are different in nature, i.e., QoS is data-driven and numerical, while QoE is expert-based and linguistic. Consequently, to predict WS QoSE, in this paper, we propose a hybrid fuzzy inference approach, composing both quantitative and qualitative data inputs into WS QoSE output by applying the adaptive neuro-fuzzy inference system (ANFIS). The developed prototype allows us to implement the proposed approach, investigate its performance, and study the effect of QoE attributes on WS QoSE. The results of the two experiments show good performance and suitability of the proposed hybrid fuzzy inference approach for predicting WS QoSE based on combining QoS and QoE attributes. We expect that those results inspire researchers and practitioners to understand the WS QoSE better and develop user needs matching WS.

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Section: Fuzzy Inference Controller (Fuzzic)mentioning

confidence: 99%

“…Since RMSE can vary in the interval [0, +∞), some authors, like [19], propose using normalized RMSE (eq. 13).…”

Section: Rm Sementioning

confidence: 99%

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

Miliauskaitė

Kalibatiene

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…In GIS and spatial analyses, several approaches were used to calculate similarity measures (see, e.g., [5][6][7][8][9][10]). Various correlation coefficients, set similarities, or distance measures are applied for this purpose [7][8][9][10][11][12]. They can be useful, for example, in data selection, cluster analysis, data harmonisation and integration, but also in data quality control [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

Ďuračiová

2023

IJGI

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The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control.

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Fuzzy Similarity and Fuzzy Inclusion Measures in Polyline Matching: A Case Study of Potential Streams Identification for Archaeological Modelling in GIS

Abstract: When combining spatial data from various sources, it is often important to

Cited by 2 publications

References 32 publications

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

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