A generic methodology for geo‐related data semantic annotation

Malik, Kaleem Razzaq; Habib, Muhammad Asif; Khalid, Shehzad; Ahmad, Mudassar; Alfawair, Mai; Ahmad, Awais; Jeon, Gwanggil

doi:10.1002/cpe.4495

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Therefore, the popularity of data stream research can be considered one of the reasons for giving emphasis to this type of data model. Based on [8], the lack of solutions to prevent data loss and the problem of compatibility are drawbacks of relational databases in converting RDB data sets to Semantic Web (SW) for storing spatial data. We compared the results of this study with the latest ranking of databases (2020) in terms of popularity.…”

Section: (D) Graph Databasementioning

confidence: 99%

“…The object-relational [7] was another model developed, still facing the scalability problem. Additionally, the explosive growth of data traffic by social networks, e-commerce as well as multimedia data streams at the sensors [8] was an emerging challenge to IT companies and other data resources. According to [9], this growth rate doubles every two years and has increased tenfold within the years 2013 to 2020 (from 4.4 to 44 ZB).…”

Section: Introductionmentioning

confidence: 99%

“…MySQL and RDF Store were used for implementation of the model. The flexibility, mobility, and efficiency were the considered features in this model.In[8], model and unifying model data resource were used in the presented two-way mapping method. To show data sources in various NoSQL data models, an integrated data model is provided and then utilized to test accessibility as a key feature in data management systems.…”

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confidence: 99%

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A Systematic Review of Data Models for the Big Data Problem

Mostajabi¹,

Safaei

Sahafi

2021

IEEE Access

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Nowadays, data are generated in a continuous streaming manner as the inputs of various applications. The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, ecommerce, social networks, and online communities. The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem. The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on. Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems' design, DB languages, etc.) rely on. So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data. It is also the essence in various areas of cloud migration, web-scale, and so forth. In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another. To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.

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Section: (D) Graph Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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A Systematic Review of Data Models for the Big Data Problem

Mostajabi¹,

Safaei

Sahafi

2021

IEEE Access

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“…At the same time, the algorithm provides reputation values also to each object as a weighted sum of the evaluation that it has received from all users. In this way, from a statistical point of view, discordant evaluations have only modest impact, and objects receive a more correct average evaluation [77]. In our scheme, to tap the full potential of iterative filtering, we consider:…”

Section: A Reputation-based Approach To Adjusting Vessel Datamentioning

confidence: 99%

Workflow-based automatic processing for Internet of Floating Things crowdsourced data

Montella

Luccio

Marcellino

et al. 2019

Future Generation Computer Systems

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Data from sensors incorporated into mobile devices, such as networked navigational sensors, can be used to capture detailed environmental information. We describe here a workflow and framework for using sensors on boats to construct unique new datasets of underwater topography (bathymetry). Starting with a large number of measurements of position, depth, etc., obtained from such an Internet of Floating Things, we illustrate how, with a specialized protocol, data can be communicated to cloud resources, even when using delayed, intermittent, or disconnected networks. We then propose a method for automatic sensor calibration based on a novel reputation approach. Sampled depth data are interpolated efficiently on a cloud computing platform in order to provide a continuously updated bathymetric database. Our prototype implementation uses the FACE-IT Galaxy workflow engine to manage network communication and exploits the computational power of GPGPUs in a virtualized cloud environment, working with a CUDA-parallel algorithm, for efficient data processing. We report on an initial evaluation involving data from a sailing vessel in Italian coastal waters.

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“…Malik et al discuss a data transformation methodology for geo‐related semantic annotation and the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data. Research on currently available tools and methodologies along with their frameworks can help for bringing state‐of‐the‐art mechanisms for data fusion and transformation.…”

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confidence: 99%

Data fusion in the internet of data

Piccialli

Jung

2018

Concurrency and Computation

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This special issue collates a selection of representative research articles that were primarily presented at the 2nd International Workshop on Data Mining on Internet of Things (IoT) Systems. This annual workshop brings together researchers and practitioners from both academia and industry who are working on data mining approaches on the IoT with applications in the Smart City framework and in the Cultural Heritage research domain in order to promote an exchange of ideas, discuss future collaborations, and develop new research directions.The Internet of Things envisages a plethora of heterogeneous objects interacting with the physical environments. It can be foreseen that IoT applications will raise the scale of data to an unprecedented level. Collecting, analyzing, and correlating data from different resources is a key role to drive smart interactions between actors of IoT environments.In this scenario, the Internet of Data (IoD) represents a concept of network composed by data entities coming from the Interne of Things (IoT).The IoD can be considered an extension of the IoT into the digital world, since the amount of data being collected is staggering. The opportunities created by IoD have the potential to be infinite. The IoD presents an ambitious purpose, ie, organizing the data to be interconnected as a network in order to infer useful information for data analysis and creates useful, customized, and location-based services.The scope of this special issue is broad and is representative of the multidisciplinary nature of the Internet of Data research field.Zheng et al 1 propose a term representation framework for four different features, ie, temporal feature, geographical feature, term co-occurrence, and hashtag. The framework deals with a series of attribution definition to detect topics on a Twitter dataset and confirm the effectiveness of LTDMF.The authors have also deeply analyze the importance of different features in topic detection.Kumar et al 2 present an efficient and secure time-limited hierarchical key assignment scheme key management suitable for data outsourcing scenario. They compare the proposed scheme with other recent similar schemes with respect to the cost of static and dynamic operations.The obtained results show that with similar unit private storage cost; the proposed scheme manages to reduce the key generation cost at the data owner and key derivation cost by each user.Wi and Tsai 3 propose a system to analyze at-home behavior and physiological indices of the elderly people. The design concepts for software and hardware equipment emphasize the following features: (1) low-cost sensor devices, (2) user-friendly interface for the wearable device, (3) easy installation for the equipment, and (4) low-power consumption for the wearable device.Experiment results show that the system simulation proves the algorithm to be feasible.Malik et al 4 discuss a data transformation methodology for geo-related semantic annotation and the importance of reducing the response time of investigation and offe...

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A generic methodology for geo‐related data semantic annotation

Cited by 9 publications

References 28 publications

A Systematic Review of Data Models for the Big Data Problem

A Systematic Review of Data Models for the Big Data Problem

Workflow-based automatic processing for Internet of Floating Things crowdsourced data

Data fusion in the internet of data

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