3 Journals in Computer Science and 3 Journals in Big Data

3 journals that focus on algorithms, programming languages, managing telecommunications software engineering, managing corporate information resources, and managing partnership-based information technology (IT) operations:

Internal Journal of e-Collaboration: http://www.igi-global.com/journal/international-journal-collaboration-ijec/1090

It is a peer-reviewed journal that studies both in theory and practical findings that relate to implementation and design of collaboration tools: email, listservs, teleconferences, automate workflow, and demand management. This is extremely vital for those managers who feel that you can be a differentiator between groups and other companies via your application of collaboration tools. It is published quarterly, since 2005.

Journal of the ACM: http://jacm.acm.org/area/Programming+Languages

Editor and peer-reviewed process that covers articles about the design, semantics, implementation, and application of programming languages. Topics discussed: Parsing, compiling, optimization (like High-Performance Computing), run-time organization, data abstraction, modularity, parallelism, concurrency, domain and category theory, database systems and theory, algorithms and data structures, Artificial Intelligence, etc. Published bimonthly, since 1954.

European Association for Programming Languages and Systems (EAPLS E-journal): http://danae.uni-muenster.de/lehre/kuchen/JFLP/

A peer-reviewed process, which covers function and logic programming, with a focus on the integration of paradigms. Been in publication since 1995 and it publishes yearly.

3 journals that cover big data:

Big Data: http://www.liebertpub.com/overview/big-data/611/

Since 2013, this is a quarterly peer-reviewed journal. Reports on the current state of storing, organizing, protecting, manipulating large data sets. It also explores challenges and opportunities in data discovery.

Intelligent Data Analysis: http://www.iospress.nl/journal/intelligent-data-analysis/

Focused on Artificial Intelligence techniques in data analytics across all disciplines: visualization of data, data pre-processing, mining techniques, tools, and apps, machine learning, neural nets, fuzzy logic, stats pattern recognition, filtering, etc. 70% of papers are applications oriented and 30% is theoretical work. Published bimonthly and since 1996.

CODATA Data Science Journal: http://datascience.codata.org/

Biannual peer-reviewed e-journal since 2002, which covers: data, databases, processing, complexity, scalability, distribution, interaction, application, interface with experiments, models, and information complexes, etc.

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