Data Matching

A continuous update of authoritative spatial databases is highly demanding task in both aspects, technical and financial. In the same time, alternative modalities to collect content, in particular spatial content, have achieved a certain maturity and must be considered as they may leverage the cost of updating authoritative spatial databases (Al-Bakri, 2010). This alternative data known as Volunteered Geographical Information – VGI (Goodchild, 2007) is easy available and is being collected in almost every moment somewhere in the world.

This paper seeks to identify and address issues that may arise with the use and analysis of the linked Longitudinal Surveys of Australian Youth (LSAY) and the National Assessment Program — Literacy and Numeracy (NAPLAN) data. The study first explores the factors affecting an LSAY respondent’s propensity to consent to the data linkage, the aim being to investigate any resultant bias in the linked data. The study also describes two associated procedures: the application of customised weights to deal with the potential bias; and a representativity analysis, which indicates how representative the consenting respondents are of the target population. Finally, the findings from the study are used as the basis of recommendations and guidance for researchers on the use of the linked LSAY-NAPLAN data in analyses.

This publication presents research highlights from the past 25 years of the LSAY program, with a focus on schooling, VET in schools programs, the influences of socioeconomic status and demographics on later opportunities, and pathways taken from school into further education and the workforce. This publication is not an exhaustive reflection of LSAY’s long research history but aims to showcase the value of the data. More than 300 studies have been published using LSAY data, and an additional 2400 studies have cited work published under the LSAY research program. Interest in LSAY data continues to increase, with 2018 and 2019 setting consecutive records for numbers of applications from prospective researchers.

NCVER is an independent body responsible for collecting, managing and analysing, evaluating and communicating research and statistics about vocational education and training (VET). NCVER's in-house research and evaluation program undertakes projects which are strategic to the VET sector. These projects are developed and conducted by NCVER's research staff and are funded by NCVER. This research aims to improve policy and practice in the VET sector.

En nuestro país la información electoral, es decir, aquella relacionada a los electores, los trámites que modifican los datos de estos, los actos electorales, los partidos políticos y sus autoridades, etc. está administrada por la Ju sticia Nacional Electoral y la Cámara Nacional Electoral. Para realizar estas funciones s e utilizan actualmente varios sistemas distintos y geográficamente descentralizados ya que se hallan instalados en cada jurisdicción.

Gathering and processing large amounts of data is increasing every day. Record linkage is one of the most complex data-intensive tasks, which is used to accurately match records from different data sources that contain information about same entity like a person, especially when they do not share common identifier. As more resources in more than one language become available, new methods are required that are capable to match records expressed in more than one language. In this paper, we are presenting a scalable, cost-aware load balancing technique over MapReduce that is capable to link records from different multilingual data sources accurately and efficiently by re-distributing the multilingual matching tasks on available machines based on their cost. We are evaluating our approach on a Hadoop cluster on cloud infrastructure against state of the art blocking-based load balancing techniques, where our approach outperforms other approaches in terms of execution time and scalability.

The very fast developments of web and data collection technologies have enabled non-experts to collect and disseminate geospatial datasets through web applications. This new type of spatial data is usually known as collaborative mapping or volunteered geographic information VGI. There are various countries around the world could benefit from collaborative mapping data because it is cost free data, easy to access and it provides more customised data. However, there is a concern about its quality because the data collectors may lack the sufficient experience and training about geospatial data production. Most previous studies which have outlined and analysed VGI quality focused on positional and linear features. The current research has been conducted to investigate the quality of another feature type such as polygons (buildings) of collaborative mapping data. Two different VGI data sources have been tested: Google Maps and WikiMapia services. The VGI data was compared with reference ...

Importing spatial open data in OpenStreetMap (OSM) project, is a practice that has existed from the beginning of the project. The rapid development and multiplication of collaborative mapping tools and open data have led to the growth of the phenomenon of importing massive data into OSM. The goal of this paper is to study the evolution of the massive imports over time. We propose an approach in three steps: classification of the sources used to edit features in the OSM platform including those massively imported, classification of modifications, and identification of evolution patterns. The approach is mixing global analysis (i.e. sources and modifications are classified) and feature based analysis (i.e. imported features are analyzed with respect to their evolution over time). The approach is applied on three datasets coming from OSM considered for their heterogeneity in terms of complexity, imports, and spatial and temporal characteristics. The results show that there is a sustained activity of edition on imported features, with a ratio between geometry editions and semantic editions depending on the type of the features, with roads being the features concentrating the most activity.

OpenStreetMap (OSM) is the most successful example of Volunteered Geographic Information (VGI). It is also the most frequently used case study in research that focuses on VGI quality, as it is usually considered a proxy for other VGI projects. The research in this area usually focuses on comparisons with authoritative data, measurements and quality statistics. In other papers, scholars have explored quality frameworks or studied the motivation and engagement of volunteers. This chapter examines OSM quality from a different point of view. The focus here is on examining how the qualitative elements of the micro-environment within OSM, such as data specifications and the OSM editors, have evolved over time. We discuss how their evolution can affect OSM data quality, taking into account a number of different factors and dimensions that directly affect the quality of the contributions.

Recommender systems (RS), as supportive tools, filter information from a massive amount of data based on the determined preferences. Most of the RS require information about the context of users such as their locations. In such cases, location-aware recommender systems (LARS) can be employed to suggest more personalized items to the users. The most current research projects on LARS focus on the development of algorithms, evaluation methods and applications. However, the role of up-to-date spatial databases in LARS is not a well-researched area. The up-to-date spatial information would potentially improve the accuracy of items which are recommended by LARS. Volunteered geographic information (VGI) could be a low-cost source of up-to-date spatial information for LARS. This article proposes an approach to enrich spatial databases of LARS by VGI. Since not all records of VGI are fitted for use in LARS, a mechanism is developed to identify useful information. Some VGI data sets refer to existing spatial data in the database while other VGI data sets are shared for the first time. Therefore, the proposed method assessed the quality of VGI with reference source (for VGI which is existed in the database) and VGI without reference source (for VGI which is shared for the first time). To demonstrate the feasibility of the proposed approach, a mobile application has been developed to recommend suitable restaurants to the users based on their geospatial locations. The evaluation of the method indicates that VGI can potentially enhance the functionality of the LARS in predicting the users' interests.

While big data benefits are numerous, most of the collected data is of poor quality and, therefore, cannot be effectively used as it is. One pre-processing the leading big data quality challenges is data duplication. Indeed, the gathered big data are usually messy and may contain duplicated records. The process of detecting and eliminating duplicated records is known as Deduplication, or Entity Resolution or also Record Linkage. Data deduplication has been widely discussed in the literature, and multiple deduplication approaches were suggested. However, few efforts have been made to address deduplication issues in Big Data Context. Also, the existing big data deduplication approaches are not handling the case of the decreasing performance of the deduplication model during the serving. In addition, most current methods are limited to duplicate detection, which is part of the deduplication process. Therefore, we aim through this paper to propose an End-to-End Big Data Deduplication Framework based on a semi-supervised learning approach that outperforms the existing big data deduplication approaches with an F-score of 98,21%, a Precision of 98,24% and a Recall of 96,48%. Moreover, the suggested framework encompasses all data deduplication phases, including data preprocessing and preparation, automated data labeling, duplicate detection, data cleaning, and an auditing and monitoring phase. This last phase is based on an online continual learning strategy for big data deduplication that allows addressing the decreasing performance of the deduplication model during the serving. The obtained results have shown that the suggested continual learning strategy has increased the model accuracy by 1,16%. Furthermore, we apply the proposed framework to three different datasets and compare its performance against the existing deduplication models. Finally, the results are discussed, conclusions are made, and future work directions are highlighted.

The record de-duplication is an important part of data cleaning process of a data-warehouse. Identification of multiple duplicate entries of a single entity in a data-warehouse is known as de-duplication. A lot of research is carried out on various aspects of record de-duplication such as use of blocking and indexing techniques, choice of blocking predicate, quality of blocking and optimization in comparison space. A special attention is required for de-duplication process in a Real-time Environment. This research attempts to address automatic token formation for real-time data de-duplication process. In the proposed approach no human intervention is required for the deduplication process. Proposed Optimized Automated Token Formation (OATF) is a two-step approach where in the former step candidates of token are generated and in the later step, optimal candidates are selected which assure maximum true positive coverage. Experimentation shows that OATF outperforms manual token formati...

Merging databases from different data sources is one of the important tasks in the data integration process. This study will integrate lecturer data from data sources in the application of academic information systems and research information systems at the Sriwijaya State Polytechnic. This integration of lecturer data will later be used as a single data as master data that can be used by other applications. Lecturer data in the academic section contains 444 records, while those from the p3m section contain 443 records. An important task in the database merging process is to eliminate duplicate records. One of the important libraries in the formation of this master data management uses the record linkage toolkit which is implemented in the python programming language. The steps taken are pre-processing, generating candidate record pairs, compare pairs, score pairs, and finally the data link to merge the two data sources. In this study, 5 fields, namely username, name, place of birth...

Volunteered Geographic Information (VGI) phenomena offer an alternative or supplement to the authoritative mechanism of geospatial data acquisition. It aims to allow people without professional geospatial skills or knowledge to participate in the geospatial data collection. VGI has been boosted by recent advances in geospatial technology and applications. VGI applications have shown great potential in various areas such as disaster management and public health. However, VGI suffers from a lack of quality assurance, because VGI contributors may lack knowledge of the geospatial domain and credibility. Moreover, VGI data may have different levels of detail and precision, and may have been collected for different purposes. Appropriate VGI data for a specific application may be less appropriate for another application. End-users may use VGI data without being aware of its appropriateness to their requirements. This may cause a risk that arises when end-users inappropriately use, or be uncertain about using VGI data in their applications. This risk may undermine the VGI project, in which end-users are involved. This paper proposes an approach that aims to enhance VGI quality assurance by measuring the spatio-semantic similarity between user requirements and provided VGI, and to evaluate the fitness-for-use of VGI. The proposed approach is based on an algorithm to help VGI end-users to deal with the risks related to the quality of VGI data. The approach helps end-user make appropriate decisions about VGI data (e.g., considering or not considering VGI data and being careful when using VGI data). INDEX TERMS Fitness-for-use, volunteered geographic information and risk management.

Record linkage is an momentous process in data soundness which is used in combining, matching and duplicate removal from more than two databases that refer to the same entities. Deduplication is the process of taking off duplicate records in a united database. Now a days,data cleaning and standardization becomes an pompous process. Due to yielding capacity of today's database, discovering matching records in united database is a crucial one. Indexing technique specifically suffix and prefix array is used to efficiently implement record linkage deduplication.

Deduplication is the process of determining all categories of information within a data set that signify the same real life / world entity. The data gathered from various resources may have data high quality issues in it. The concept to identify duplicates by using windowing and blocking strategy. The objective is to achieve better precision, good efficiency and also to reduce the false positive rate all are in accordance with the estimated similarities of records. Various Similarity metrics are commonly used to recognize the similar field entries. So the main focus of this paper is to applying appropriate similarity measure on appropriate data to properly identifying the duplicates.

Information is united for common purpose from many sidedness computerized files is referred as record linkage. The basic methods compare name and address information across pairs of files to determine those pairs of records that are associated with the same entity. An entity might be a business, a person, or some other type of unit that is listed. De-duplication is a scold of identifying one or more records in receptacle which represents same object or entity. The same data may be depicting in different way in all possible database causing problem. Diverse indexing techniques have been elaborated for record linkage and de-duplication, in modern time. They are intended to reducing the number of record pairs to be compared in similarity matching process, while at the same time maintaining high matching quality. This paper presents, contraption of suffix array blocking for efficacious record linkage and de-duplication based on different similarity measures.

Record linkage is an momentous process in data soundness which is used in combining, matching and duplicate removal from more than two databases that refer to the same entities. Deduplication is the process of taking off duplicate records in a united database. Now a days,data cleaning and standardization becomes an pompous process. Due to yielding capacity of today's database, discovering matching records in united database is a crucial one. Indexing technique specifically suffix and prefix array is used to efficiently implement record linkage deduplication.

Merging databases from different data sources is one of the important tasks in the data integration process. This study will integrate lecturer data from data sources in the application of academic information systems and research information systems at the Sriwijaya State Polytechnic. This integration of lecturer data will later be used as a single data as master data that can be used by other applications. Lecturer data in the academic section contains 444 records, while those from the p3m section contain 443 records. An important task in the database merging process is to eliminate duplicate records. One of the important libraries in the formation of this master data management uses the record linkage toolkit which is implemented in the python programming language. The steps taken are pre-processing, generating candidate record pairs, compare pairs, score pairs, and finally the data link to merge the two data sources. In this study, 5 fields, namely username, name, place of birth...

Traditionally, national mapping agencies produced datasets and map products for a low number of specified and internally consistent scales, i.e. at a common level of detail (LoD). With the advent of projects like OpenStreetMap, data users are increasingly confronted with the task of dealing with heterogeneously detailed and scaled geodata. Knowing the scale of geodata is very important for mapping processes such as for generalization of label placement or land-cover studies for instance. In the following chapter, we review and compare two concurrent approaches at automatically assigning scale to OSM objects. The first approach is based on a multi-criteria decision making model, with a rationalist approach for defining and parameterizing the respective criteria, yielding five broad LoD classes. The second approach attempts to identify a single metric from an analysis process, which is then used to interpolate a scale equivalence. Both approaches are combined and tested against well-known Corine data, resulting in an improvement of the scale inference process. The chapter closes with a presentation of the most pressing open problems

Whereas it was possible to define the level of detail (LoD) of authoritative datasets, it is not possible for Volunteered Geographic Information (VGI), often characterised by heterogeneous levels of details. This heterogeneity is a curb for mapmaking, particularly when using traditional map derivation processes such as generalisation. The paper proposes a method to infer the level of detail of VGI features. Then, inconsistencies between features with different levels of detail that get in the way of good mapmaking can be automatically identified. Some proposals are made to harmonise level of detail heterogeneities. The LoD inference is implemented and results are presented on OpenStreetMap data.

Whereas it was possible to define the level of detail (LoD) of authoritative datasets, it is not possible for Volunteered Geographic Information (VGI), often characterised by heterogeneous levels of details. This heterogeneity is a curb for mapmaking, particularly when using traditional map derivation processes such as generalisation. The paper proposes a method to infer the level of detail of VGI features. Then, inconsistencies between features with different levels of detail that get in the way of good mapmaking can be automatically identified. Some proposals are made to harmonise level of detail heterogeneities. The LoD inference is implemented and results are presented on OpenStreetMap data.

The concept of Volunteered Geographic Information (VGI) has recently emerged from the new Web 2.0 technologies. The OpenStreetMap project is currently the most significant example of a system based on VGI. It aims at producing free vector geographic databases using contributions from Internet users. Spatial data quality becomes a key consideration in this context of freely downloadable geographic databases. This article studies the quality of French OpenStreetMap data. It extends the work of Haklay to France, provides a larger set of spatial data quality element assessments (i.e. geometric, attribute, semantic and temporal accuracy, logical consistency, completeness, lineage, and usage), and uses different methods of quality control. The outcome of the study raises questions such as the heterogeneity of processes, scales of production, and the compliance to standardized and accepted specifications. In order to improve data quality, a balance has to be struck between the contributors' freedom and their respect of specifications. The development of appropriate solutions to provide this balance is an important research issue in the domain of user-generated content.

En nuestro país la información electoral, es decir, aquella relacionada a los electores, los trámites que modifican los datos de estos, los actos electorales, los partidos políticos y sus autoridades, etc. está administrada por la Justicia Nacional Electoral y la Cámara Nacional Electoral. Para realizar estas funciones se utilizan actualmente varios sistemas distintos y geográficamente descentralizados ya que se hallan instalados en cada jurisdicción.

; diego.silva; roberto.moreno; pablo.masier; matias.codorniu}@fce.uncu.edu.ar http://fce.uncuyo.edu.ar Resúmen: el estudio de las anomalías en los días de la semana del mercado de las criptodivisas completa una vasta literatura que analiza anomalías en los calendarios. Los modelos generados para el estudio de la anomalía contempla el análisis con datos que no responden a una distribución normal. Se utiliza una transformación adecuada para ajustar un modelo normal a los datos y lograr los tres supuestos: normalidad, homocedasticidad e independencia, analizando si aún así se puede evidenciar el efecto del día de la semana, y se realiza un test ajustando el modelo para el no cumplimiento del supuesto normalidad, y se concluye que persisten en los modelos, las anomalías en los días de la semana.

Actualmente en las administraciones tributarias existe un gran volumen de datos. Estos datos contienen implícito un conocimiento que puede ser extraído, este conocimiento dependerá de la calidad de los datos, y en esa cantidad de datos no puede concebirse que no posean anomalías. Los datos tienen anomalías, y las mismas responden a distintas causales. El análisis de las causales en las anomalías de los datos permitirá deducir si ellas responden a ilícitos o hechos de corrupción.

Microscopic traffic flow simulations as tools for enabling detailed insights on traffic efficiency and safety gained numerous popularity among transportation researchers, planners and engineers in the first to decades of the 21st century. By implementing a test bed for simulation scenarios of complex urban transportation infrastructure it is possible to inspect specific effects of introducing small infrastructural changes related to the built environment and to the introduction of advanced traffic control strategies. The possibility of reproducing present problems or the transportation services, such as the ones of public bus services is a key motivation of this work. In this research, we reproduce the road network of the city of Kyoto for observing specific travel patterns of public buses such as the bus bunching phenomena. Therefore, a selection of currently available data sets is used for calibrating a cutout of the Kyoto road network of a relatively large extent. After introduci...

Record linkage is the problem of identifying similar records across different data sources. The similarity between two records is defined based on domain-specific similarity functions over several attributes. De-duplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim is to match all records relating to the same entity. Different measures have been used to characterize the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and de-duplication quality and complexity. A matching tree is used to overcome communication overhead and give matching decision as obtained using the conventional linkage technique. Developed new indexing techniques for scalable record linkage and de-duplication techniques into the febrl framework, as well as the investigation of learning techniques for efficient and accurate indexing.

The assessment of data quality from different sources can be considered as a key challenge in supporting effective geospatial data integration and promoting collaboration in mapping projects. This paper presents a methodology for assessing positional and shape quality for authoritative large-scale data, such as Ordnance Survey (OS) UK data and General Directorate for Survey (GDS) Iraq data, and Volunteered Geographic Information (VGI), such as OpenStreetMap (OSM) data, with the intention of assessing possible integration. It is based on the measurement of discrepancies among the datasets, addressing positional accuracy and shape fidelity, using standard procedures and also directional statistics. Line feature comparison has been undertaken using buffering techniques and statistics, whilst shape metrics, including moments invariant, have been applied to assess polygon matching. The analyses are presented with a user-friendly interface which eases data input, computation and output of results, and assists in interpretation of the comparison. The results show that a comparison of positional and shape characteristics of OS data or GDS data, with those of OSM data, indicates that their integration for large scale mapping applications is not viable.

Points of interests (POIs) describe a geographic entity that users are focused on such as a school. The different types of POIs are represented by the cartographic symbols. Its positional accuracy on a map is usually considered good if the symbols are inside a polygon feature. However, the correct location for the POIs is the place close to the front edge of building footprints. In this study, we aim to develop an algorithm which identifies the possible front edges of buildings by considering the geometry of the network and building blocks in urban areas. The building blocks are derived from amalgamating the footprints near to each other in the partitions obtained from the street network. The each line segment is extracted from the blocks, and its probability of being on the front edge is calculated. Finally, the results are compared by the real statuses obtained from the street perspectives.

Microscopic traffic flow simulations as tools for enabling detailed insights on traffic efficiency and safety gained numerous popularity among transportation researchers, planners and engineers in the first to decades of the 21 st century. By implementing a test bed for simulation scenarios of complex urban transportation infrastructure it is possible to inspect specific effects of introducing small infrastructural changes related to the built environment and to the introduction of advanced traffic control strategies. The possibility of reproducing present problems or the transportation services, such as the ones of public bus services is a key motivation of this work. In this research, we reproduce the road network of the city of Kyoto for observing specific travel patterns of public buses such as the bus bunching phenomena. Therefore, a selection of currently available data sets is used for calibrating a cutout of the Kyoto road network of a relatively large extent. After introducing a method for geodata extraction and conversion, we approach the calibration by introducing virtual detectors representing present inductive loops and make use of historical traffic count records. Additionally, we introduce bus routes partially contributed by volunteer mappers (OSM project). First simulation outcomes show numerous familiar (local knowledge) flow patterns.

The purpose of data integration is to integrate the multiple sources of heterogeneous data available on the internet, such as text, image, and video. After this stage, the data becomes large. Therefore, it is necessary to analyze the data that can be used for the efficient execution of the query. However, we have problems with solving entities, so it is necessary to use different techniques to analyze and verify the data quality in order to obtain good data management. Then, when we have a single database, we call this mechanism deduplication. To solve the problems above, we propose in this article a method to calculate the similarity between the potential duplicate data. This solution is based on graphics technology to narrow the search field for similar features. Then, a composite mechanism is used to locate the most similar records in our database to improve the quality of the data to make good decisions from heterogeneous sources.

Where the streets have no name is probably the preferred place for a volunteer OpenStreetMapper. Launched in 2004, the Open Street Map project aimed to share geographical data based on volunteer mapping and led to the collection of geographical data from almost every country in the world within fifteen years. The increased dissemination of cartographic data via the Internet has been helpful in real life, socially, and has resulted in the number of published documents increasing rapidly. To evaluate the impact of volunteered cartography on scientific research, a science mapping approach was applied to the published literature on the Open Street Map project on the basis of co-occurrence and co-citation analyses, which showed that the main themes (conceptual network) were of technical relevance, collaboration among scholars and among institutes (social network) was not strong, and knowledge and ideas circulated within a limited network. In this study, documents published by OpenStreetM...

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitabl...

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitabl...

Duplicate record is a known problem within the datasets especially within databases of huge volumes. The accuracy of duplicates detection determines the efficiency of the duplicates removal process. Unfortunately, the effort to detect duplicates becomes more challenging due to the presence of missing values within the records. This is because, during the clustering and matching process, missing values can cause records that are similar to be assigned in a wrong group, causing the duplicates left undetected. In this paper, we present how duplicates detection can be improved even though missing values are present within a data set using our Duplicates Detection within the Incomplete Data set (DDID) method. We hypothetically add the missing values to the key attributes of two datasets under study, using an arbitrary pattern to simulate both complete and incomplete data sets. We analyze the results to evaluate the performance of duplicates detection using the Hot Deck method to compensa...

Entity resolution, the process of determining if two or more references correspond to the same entity, is an emerging area of study in computer science. While entity resolution models leverage artificial intelligence, machine learning, and data mining techniques, relationships between various models remain ill-specified. Despite growth in both research and literature, investigations are scattered across communities with minimal communication. This paper introduces a conceptual framework, called ENRES, for explicit and formal ...

With the exception of the Commonwealth Coat of Arms: (for terms of use, refer to <www.itsanhonour.gov.au/coat-arms/index.cfm>), the details of the relevant licence conditions are available on the Creative Commons website, as is the full legal code for the CC-BY 3.0 AU licence (see: <www.creativecommons.org/licenses/by/3.0/au/legalcode>). The opinions, comments and/or analysis expressed in this document are those of the authors and may not reflect those of the Australian Government or the Australian Institute of Family Studies and cannot be taken in any way as expressions of government policy. Administrative Arrangements Orders changes On 18 September 2013 the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) was renamed the Department of Social Services (DSS). References in this publication to FaHCSIA should be read in that context.

The purpose of data integration is to integrate the multiple sources of heterogeneous data available on the internet, such as text, image, and video. After this stage, the data becomes large. Therefore, it is necessary to analyze the data that can be used for the efficient execution of the query. However, we have problems with solving entities, so it is necessary to use different techniques to analyze and verify the data quality in order to obtain good data management. Then, when we have a single database, we call this mechanism deduplication. To solve the problems above, we propose in this article a method to calculate the similarity between the potential duplicate data. This solution is based on graphics technology to narrow the search field for similar features. Then, a composite mechanism is used to locate the most similar records in our database to improve the quality of the data to make good decisions from heterogeneous sources.

With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper, we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development (RCMRD)) and non-authoritative (data from OSM and Google's Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all of these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

The assessment of the quality and accuracy of Volunteered Geographic Information (VGI) contributions, and by extension the ultimate utility of VGI data has fostered much debate within the geographic community. The limited research to date has been focused on VGI data of linear features and has shown that the error in the data is heterogeneously distributed. Some have argued that data produced by numerous contributors will produce a more accurate product than an individual and some research on crowd-sourced initiatives has shown that to be true, although research on VGI is more infrequent. This paper proposes a method for quantifying the completeness and accuracy of a select subset of infrastructure-associated point datasets of volunteered geographic data within a major metropolitan area using a national geospatial dataset as the reference benchmark with two datasets from volunteers used as test datasets. The results of this study illustrate the benefits of including quality control in the collection process for volunteered data.

El presente artículo describe las etapas definidas en la Agencia de Recaudación de la Provincia de Buenos Aires para alcanzar un Gobierno de Datos sobre la información de los contribuyentes de la Provincia de Buenos Aires. El proyecto hace foco principalmente en aplicar un proceso de mejora continua sobre la calidad de los datos del padrón de contribuyentes y todos los impuestos relacionados con le objetivos de establecer un proceso que permita definir políticas de gobierno, controles de calidad, procesos de corrección que permitan mejorar la operación, transferencia, calidad y precisión de los servicios al contribuyente en pos de poner a disposición información con un alto nivel de exactitud, completitud, integridad, coherencia y confiabilidad.

The purpose of data integration is to integrate the multiple sources of heterogeneous data available on the internet, such as text, image, and video. After this stage, the data becomes large. Therefore, it is necessary to analyze the data that can be used for the efficient execution of the query. However, we have problems with solving entities, so it is necessary to use different techniques to analyze and verify the data quality in order to obtain good data management. Then, when we have a single database, we call this mechanism deduplication. To solve the problems above, we propose in this article a method to calculate the similarity between the potential duplicate data. This solution is based on graphics technology to narrow the search field for similar features. Then, a composite mechanism is used to locate the most similar records in our database to improve the quality of the data to make good decisions from heterogeneous sources.

The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both th...

In many countries, geospatial data are typically provided by public institutions. Cities have been mapped using such public data. On the other hand, the demand for geospatial data has been diversifying, given the requirements for mapping cities. To respond to demands for new geospatial data, creation of citizen-generated open data and volunteered geographic information (VGI) have recently become popular. However, the quality of such open data and VGI are not always guaranteed. The number of studies on quality assessments of VGI have increased in recent years. The present study aimed to identify OpenStreetMap (OSM), one type of VGI, as well as contributors' awareness of data quality, and the relationships between their awareness and the positional accuracy of the OSM data contributed by them. The results showed that awareness or lack of the positional accuracy did not affect the quality of the OSM data created by the contributors. These findings suggest that the crowdsourcing approach might not guarantee the data quality of VGI.

In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distancebased coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.

Points of interests (POIs) describe a geographic entity that users are focused on such as a school. The different types of POIs are represented by the cartographic symbols. Its positional accuracy on a map is usually considered good if the symbols are inside a polygon feature. However, the correct location for the POIs is the place close to the front edge of building footprints. In this study, we aim to develop an algorithm which identifies the possible front edges of buildings by considering the geometry of the network and building blocks in urban areas. The building blocks are derived from amalgamating the footprints near to each other in the partitions obtained from the street network. The each line segment is extracted from the blocks, and its probability of being on the front edge is calculated. Finally, the results are compared by the real statuses obtained from the street perspectives.

Accurate and efficient entity resolution (ER) is a significant challenge in many data mining and analysis projects requiring integrating and processing massive data collections. It is becoming increasingly important in real-world applications to develop ER solutions that produce prompt responses for entity queries on large-scale databases. Some of these applications demand entity query matching against large-scale reference databases within a short time. We define this as the query matching problem in ER in this work. Indexing or blocking techniques reduce the search space and execution time in the ER process. However, approximate indexing techniques that scale to very large-scale datasets remain open to research. In this paper, we investigate the query matching problem in ER to propose an indexing method suitable for approximate and efficient query matching. We first use spatial mappings to embed records in a multidimensional Euclidean space that preserves the domain-specific simil...