Data Lifecycle: From Big Data to SmartData

Arass, Mohammed El; Souissi, Nissrine

doi:10.1109/cist.2018.8596547

Cited by 29 publications

(29 citation statements)

References 15 publications

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“…The new global gold is called 'data'. Today, the effectiveness of a project, an organization or a country is measured by its ability to collect, store and analyze, as quickly as possible, a large amount of data.This race for data collection has led to the emergence of several methods of data collection, integrating new information and communication technologies, some more complex than others [1], [2], [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Open Data Kit: Mobile Data Collection Framework For Developing Countries

Bokonda¹,

Ouazzani-Touhami²,

Souissi³

2019

IJITEE

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To evaluate and improve the delivery of health care services and programs; to collect and share information on agro-biodiversity; to fill the gaps in the data of artisanal or industrial fisheries, the use of computer tools of data collection is a major asset. It is now obvious that most of these tools are difficult to use in developing countries. And this is because of the complexity of use, either because they are commercial solutions or simply because these tools require a stable internet connection. This study is the result of thorough research on Open Data Kit (ODK), a suite of tools fully compatible with the context of developing countries and fully open source software. This article traces the research work done for the design, implementation, and improvement of ODK. A description of the different ODK extensions that can be found in the literature is given in addition to the reasons that explain the success of ODK in developing countries. To highlight the utility of ODK, an analysis of the areas of use of ODK has been made. This analysis concludes that ODK is mainly used to improve the intervention effectiveness of health system agents in developing countries.

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Section: Introductionmentioning

confidence: 99%

Open Data Kit: Mobile Data Collection Framework For Developing Countries

Bokonda¹,

Ouazzani-Touhami²,

Souissi³

2019

IJITEE

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“…"Smart" meaning the aforementioned knowledge obtained from the big and initially unstructured data set. According to [11] "the phases which constitute the data life cycle in a Big Data context are very complex. Each phase is considered as one or more complex, operational, and independent processes, but these processes are linked to one another and to make data management more flexible and smart".…”

Section: Related Workmentioning

confidence: 99%

“…Thus VMC is able to normalize accrued information and perform environmental calculations taking the relevant variables into consideration, e.g., Target Distribution (T D ) or Confidentiality, Integrity and Accessibility (CIA) triad. In addition, due to application of Smart Data algorithms [11,12] the developed VMC is capable of presenting results almost in real time to stakeholders. The small delay depends on computational resources available and the amount of data coming from a vulnerability scan.…”

Section: Introductionmentioning

confidence: 99%

“…An additional novel aspect of the present paper unfolds in the context of data life cycles presented in [11], which pertains to Smart Data and consists in retrieving knowledge from "the mass of initially unstructured data" [12] collected by VMCs, i.e., the results of vulnerability scanning, vulnerability related classification of data stemming from several publicly accessible databases and integration with information on organization's assets within the organization-specific context. In this contribution, the initially completely unrelated data, gathered by VMC is structured, normalized and filtered to bring in value and novel knowledge relevant specifically to vulnerability management.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment

et al. 2020

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The time gap between public announcement of a vulnerability—its detection and reporting to stakeholders—is an important factor for cybersecurity of corporate networks. A large delay preceding an elimination of a critical vulnerability presents a significant risk to the network security and increases the probability of a sustained damage. Thus, accelerating the process of vulnerability identification and prioritization helps to red the probability of a successful cyberattack. This work introduces a flexible system that collects information about all known vulnerabilities present in the system, gathers data from organizational inventory database, and finally integrates and processes all collected information. Thanks to application of parallel processing and non relational databases, the results of this process are available subject to a negligible delay. The subsequent vulnerability prioritization is performed automatically on the basis of the calculated CVSS 2.0 and 3.1 scores for all scanned assets. The environmental CVSS vector component is evaluated accurately thanks to the fact that the environmental data is imported directly from the organizational inventory database.

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“…A number of researches focus on data lifecycle improvement to have the best quick solution for client request (El Arass et al, 2017;El Arass and Souissi, 2018). Tracking this goal, several studies like [ (Sangroya and Singhal, 2015;Cai et al, 2016;Chen et al, 2014b;Trnka, 2014;Cavanillas et al, 2016) focus essentially on equipment for storage, computation and algorithms for the analysis to increase the capacity and speed.…”

Section: Related Workmentioning

confidence: 99%

Meta-Analysis of Data Collect Methods

Tikito¹,

Arass²,

Souissi³

2019

Journal of Computer Science

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Several organizations from different sectors depend increasingly on knowledge extracted from huge volumes of data generated by different sources, such as IoT, sensors and databases. At the core of data lifecycle, data reliability, analytics, security, scalability and use are important concerns. Coping with these issues in handling data requires understanding the challenges associated with it. Analysis process and storage devices have been widely studied. However, very few studies have explored the collect data phase. In this study we aim to analyse more the collect phase of data lifecycle to provide an optimized and smart approach. This paper aim to provide the right method to follow in data collect phase within different domain according to client needs and requirements. It provides not only a detailed view of the main steps, but also based on a prior literature review on different existing methods. This allowed us subsequently to establish a correspondence with the SLR method on which we based our method. We use an explicit example to illustrate the steps of our method.

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Data Lifecycle: From Big Data to SmartData

Cited by 29 publications

References 15 publications

Open Data Kit: Mobile Data Collection Framework For Developing Countries

Open Data Kit: Mobile Data Collection Framework For Developing Countries

Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment

Meta-Analysis of Data Collect Methods

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