Pub Date : 2021-11-24DOI: 10.5772/intechopen.100465
Omer Hassan Abdelrahman
This chapter explores the concept of open data with a focus on Open Government Data (OGD). The chapter presents an overview of the development and practice of Open Government Data at the international level. It also discusses the advantages and benefits of Open Government Data. The scope and characteristics of OGD, in addition to the perceived risks, obstacles and challenges are also presented. The chapter closes with a look at the future of open data and open government data in particular. The author adopted literature review as a method and a tool of data collection for the purpose of writing this chapter.
{"title":"Open Government Data: Development, Practice, and Challenges","authors":"Omer Hassan Abdelrahman","doi":"10.5772/intechopen.100465","DOIUrl":"https://doi.org/10.5772/intechopen.100465","url":null,"abstract":"This chapter explores the concept of open data with a focus on Open Government Data (OGD). The chapter presents an overview of the development and practice of Open Government Data at the international level. It also discusses the advantages and benefits of Open Government Data. The scope and characteristics of OGD, in addition to the perceived risks, obstacles and challenges are also presented. The chapter closes with a look at the future of open data and open government data in particular. The author adopted literature review as a method and a tool of data collection for the purpose of writing this chapter.","PeriodicalId":376330,"journal":{"name":"Open Data [Working Title]","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125613180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.5772/intechopen.99636
F. Jemili, Hajer Bouras
In today’s world, Intrusion Detection System (IDS) is one of the significant tools used to the improvement of network security, by detecting attacks or abnormal data accesses. Most of existing IDS have many disadvantages such as high false alarm rates and low detection rates. For the IDS, dealing with distributed and massive data constitutes a challenge. Besides, dealing with imprecise data is another challenge. This paper proposes an Intrusion Detection System based on big data fuzzy analytics; Fuzzy C-Means (FCM) method is used to cluster and classify the pre-processed training dataset. The CTU-13 and the UNSW-NB15 are used as distributed and massive datasets to prove the feasibility of the method. The proposed system shows high performance in terms of accuracy, precision, detection rates, and false alarms.
{"title":"Intrusion Detection Based on Big Data Fuzzy Analytics","authors":"F. Jemili, Hajer Bouras","doi":"10.5772/intechopen.99636","DOIUrl":"https://doi.org/10.5772/intechopen.99636","url":null,"abstract":"In today’s world, Intrusion Detection System (IDS) is one of the significant tools used to the improvement of network security, by detecting attacks or abnormal data accesses. Most of existing IDS have many disadvantages such as high false alarm rates and low detection rates. For the IDS, dealing with distributed and massive data constitutes a challenge. Besides, dealing with imprecise data is another challenge. This paper proposes an Intrusion Detection System based on big data fuzzy analytics; Fuzzy C-Means (FCM) method is used to cluster and classify the pre-processed training dataset. The CTU-13 and the UNSW-NB15 are used as distributed and massive datasets to prove the feasibility of the method. The proposed system shows high performance in terms of accuracy, precision, detection rates, and false alarms.","PeriodicalId":376330,"journal":{"name":"Open Data [Working Title]","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124274275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-19DOI: 10.5772/intechopen.100234
V. Kakulapati
The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need.
{"title":"Knowledge Extraction from Open Data Repository","authors":"V. Kakulapati","doi":"10.5772/intechopen.100234","DOIUrl":"https://doi.org/10.5772/intechopen.100234","url":null,"abstract":"The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need.","PeriodicalId":376330,"journal":{"name":"Open Data [Working Title]","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122348825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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