Dear Colleagues,

Storing, analysing, and accessing data is a growing problem for organisations. Competitive pressures and new regulations are requiring organisations to efficiently handle increasing volumes and varieties of data, but this does not come cheap. Data sets grow rapidly

in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. Data are analyzed in order to derive business-relevant findings and support business decisions. In order for the derived results to be valid and valuable, it is essential to ensure the quality of the underlying data. Tools, Techniques, Methods and Algorithms can help identify the data quality for different applications. Data quality, integrity and security are of prime importance in deriving any results. Information systems, artificial intelligence (AI), visualization, and statistics have a wide range of applications on data usage. For example, many organisations are faced with the challenge of transferring processes, IT systems and data structures to integate artificial intelligence technologies. The benefits of artificial intelligence such as machine learning, deep learning, and knowledge graphs enable the fast and format-free connection of different data sources simply by storing translation tables. This allows organisations to start with small set of data sources to systematically set up AI and data separately from one another, and develop and connect additional data sources in quick sprints. This enables quick success with existing data and systems, which minimizes risks and shows the benefits of AI after short development cycles.

We invite you to submit high-quality contributions to this special issue on “New Advances in Securing Data and Big Data”, the topics of which cover the entire range from theory to application:

• Data Quality 
• Big Data 
• Artificial Intelligence Algorithms 
• Data Mining and Text Mining 
• Scientific Knowledge Graph 
• Securing Big Data 
• Big Data Processing and Analytics 
• Modeling data and Process Quality 
• Machine learning for Data Analysis

Dr. Meena Jha
Dr. Kewei Sha
Dr. Otmane Azeroual
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Data Quality 
• Big Data 
• Artificial Intelligence Algorithms 
• Data Mining and Text Mining 
• Scientific Knowledge Graph 
• Securing Big Data 
• Big Data Processing and Analytics 
• Modeling data and Process Quality 
• Machine learning for Data Analysis