The staggering growth in Internet of Things (IoT) technologies is the key driver for generation of massive raw data streams in big data environments. This huge collection of raw data streams in big data systems increases computational complexity and resource consumption in cloud-enabled data mining systems. In this paper, we are introducing the concept of pattern-based data sharing in big data environments. The proposed methodology enables local data processing near the data sources and transforms the raw data streams into actionable knowledge patterns. These knowledge patterns have dual utility of availability of local knowledge patterns for immediate actions as well as for participatory data sharing in big data environments. The proposed concept has the wide potential to be applied in numerous application areas.
목차
Abstract 1. Introduction 2. Related Work 3. Big Data Problem 3.1 Big Data Complexity 4. Pattern-based Data Sharing 5. Advantages of Pattern-based Data Sharing in Big Data Environments 6. Future Application Areas 7. Conclusion References
키워드
big dataedge computingcloud computinginternet of things.
저자
Muhammad Habib ur Rehman [ Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia ]
Aisha Batool [ Department of Computing and Technology, Iqra University, Islamabad, Pakistan ]
보안공학연구지원센터(IJDTA) [Science & Engineering Research Support Center, Republic of Korea(IJDTA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Database Theory and Application
간기
격월간
pISSN
2005-4270
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Database Theory and Application Vol.8 No.4