Results 61 to 70 of about 10,119 (225)

Exploring the Power of Machine Learning in Analysing Protein–Protein Sequences

IET Systems Biology, Volume 20, Issue 1, January/December 2026.
Figure 2 depicts the structure of a peptide bond formed between amino acids to form a polypeptide chain. ABSTRACT Proteins are fundamental biological macromolecules responsible for regulating nearly all cellular processes, and their functions are largely determined by the underlying amino acid sequences.
Anindya Nag, Riya Sil, Md. Mehedi Hassan, Tamanna Haque Ritu, Biva Das, Nilanjana Roy, Anurag Sinha, Dron Guin, Nishchal Maurya   +8 more
wiley   +1 more source

MOON: MapReduce On Opportunistic eNvironments [PDF]

, 2009
—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer
Feng, Wu-chun, Archuleta, Jeremy, Zhang, Zhe, Lin, Heshan, Ma, Xiaosong, Gardner, Mark   +5 more
core   +1 more source

Network Motif Detection: Algorithms, Parallel and Cloud Computing, and Related Tools

Tsinghua Science and Technology, 2013
Network motif is defined as a frequent and unique subgraph pattern in a network, and the search involves counting all the possible instances or listing all patterns, testing isomorphism known as NP-hard and large amounts of repeated processes for ...
Wooyoung Kim, Martin Diko, Keith Rawson
doaj   +1 more source

The Performance Optimization of Big Data Processing by Adaptive MapReduce Workflow

IEEE Access, 2022
The discussion context of this paper is big data processing of MapReduce by volunteer computing in dynamic and opportunistic environments. This paper conducts a series of simulations to explore the relationship between the overall performance of ...
Wei Li, Maolin Tang
doaj   +1 more source

Three Algorithms for Parallel Graph Summarization

Expert Systems, Volume 43, Issue 1, January 2026.
ABSTRACT Most graph summarization algorithms are tailored to a specific graph summary model and were designed for one‐time computations only, that is, batch‐based computations. We developed a universal approach for parallel graph summarization and three algorithms to compute graph summaries—a batch‐based algorithm for static graphs, an incremental ...
Till Blume, Jannik Rau, David Richerby, Ansgar Scherp   +3 more
wiley   +1 more source

Primitive per l'Analisi di Grandi Grafi in MapReduce [PDF]

, 2022
I grafi sono strutture utilizzate in ogni ambito. Per l’analisi di grafi sempre più grandi è necessario sviluppare degli algoritmi paralleli. MapReduce è un modello di computazione che permette di sviluppare in modo semplice algoritmi efficienti che ...
Parigi Bini, Gianmaria
core  

Design of a TSK Rule‐Based Model With Granular Rules and Ensemble Learning in Big Data

Complexity, Volume 2026, Issue 1, 2026.
Nowadays, the management and analysis of big data have become major challenges for researchers in the field of data mining. The increasing rate of data generation, along with the need to extract meaningful patterns, highlights the necessity of developing scalable big data analysis methods.
Mohammad Nematpour, Farnaz Mahan, Witold Pedrycz, Habib Izadkhah, Pramita Mishra   +4 more
wiley   +1 more source

Experimenting sensitivity-based anonymization framework in apache spark

Journal of Big Data, 2018
One of the biggest concerns of big data and analytics is privacy. We believe the forthcoming frameworks and theories will establish several solutions for the privacy protection.
Mohammed Al-Zobbi, Seyed Shahrestani, Chun Ruan   +2 more
doaj   +1 more source

CloudDOE: a user-friendly tool for deploying Hadoop clouds and analyzing high-throughput sequencing data with MapReduce. [PDF]

PLoS ONE, 2014
BackgroundExplosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis.
Wei-Chun Chung, Chien-Chih Chen, Jan-Ming Ho, Chung-Yen Lin, Wen-Lian Hsu, Yu-Chun Wang, D T Lee, Feipei Lai, Chih-Wei Huang, Yu-Jung Chang   +9 more
doaj   +1 more source

Anti-combining for MapReduce [PDF]

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, 2014
We propose Anti-Combining, a novel optimization for MapReduce programs to decrease the amount of data transferred from mappers to reducers. In contrast to Combiners, which decrease data transfer by performing reduce work on the mappers, Anti-Combining shifts mapper work to the reducers.
Alper Okcan, Mirek Riedewald
openaire   +1 more source