Results 81 to 90 of about 36,780 (203)
Clustering‐based recommendation method with enhanced grasshopper optimisation algorithm
CAAI Transactions on Intelligence Technology, Volume 10, Issue 2, Page 494-509, April 2025.Abstract In the era of big data, personalised recommendation systems are essential for enhancing user engagement and driving business growth. However, traditional recommendation algorithms, such as collaborative filtering, face significant challenges due to data sparsity, algorithm scalability, and the difficulty of adapting to dynamic user preferences.
Zihao Zhao +9 more
wiley +1 more source
Procedia Computer Science, 2013 AbstractRecent innovations in Big Data have enabled major strides forward in our ability to glean important insights from massive amounts of data, and to use these insights to make better decisions. Underlying many of these innovations is a computational paradigm known as MapReduce, which enables computational processes to be scaled up to very large ...
openaire +1 more source
Scather: programming with multi-party computation and MapReduce [PDF]
, 2015 We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of ...
Bestavros, Azer +2 more
core +1 more source
Big data: modern approaches to storage and analysis
Труды Института системного программирования РАН, 2018 Big data challenged traditional storage and analysis systems in several new ways. In this paper we try to figure out how to overcome this challenges, why it's not possible to make it efficiently and describe three modern approaches to big data handling ...
Pavel Klemenkov, Sergey Kuznetsov
doaj +1 more source
DualTable: A Hybrid Storage Model for Update Optimization in Hive
, 2014 Hive is the most mature and prevalent data warehouse tool providing SQL-like interface in the Hadoop ecosystem. It is successfully used in many Internet companies and shows its value for big data processing in traditional industries.
Hu, Songlin +8 more
core +1 more source
Actors vs Shared Memory: two models at work on Big Data application frameworks [PDF]
, 2015 This work aims at analyzing how two different concurrency models, namely the shared memory model and the actor model, can influence the development of applications that manage huge masses of data, distinctive of Big Data applications.
Crafa, Silvia, Tronchin, Luca
core
Finding Top- $k$ Dominance on Incomplete Big Data Using MapReduce Framework
IEEE Access, 2018 Incomplete data is one major kind of multi-dimensional dataset that has random-distributed missing nodes in its dimensions. It is very difficult to retrieve information from this type of dataset when it becomes large.
Payam Ezatpoor +3 more
doaj +1 more source
, 2016 Current market tendencies show the need of storing and processing rapidly growing amounts of data. Therefore, it implies the demand for distributed storage and data processing systems.
Kaluzka, Justyna
core +1 more source
Proceedings of the 7th International Conference on Security of Information and Networks, 2014 MapReduce is a new parallel programming paradigm proposedto process large amount of data in a distributed setting.Since its introduction, there have been efforts to improvethe architecture of this model making it more efficient,secure and scalable. In parallel with these developments,there are also efforts to implement and deploy MapReduce,and one of ...
Zhang, Ning, Lahmer, Ibrahim
openaire +2 more sources
A privacy-preserving parallel and homomorphic encryption scheme
Open Physics, 2017 In order to protect data privacy whilst allowing efficient access to data in multi-nodes cloud environments, a parallel homomorphic encryption (PHE) scheme is proposed based on the additive homomorphism of the Paillier encryption algorithm. In this paper
Min Zhaoe, Yang Geng, Shi Jingqi
doaj +1 more source