Information System Engineering (ISE) is a multidisciplinary field that focuses on the analysis, design, development, and integration of systems enabling the effective and efficient use, generation, and application of information in today’s highly dynamic environment. Building on the disciplines of software engineering, systems engineering, information assurance, and networking, ISE employs techniques and methodologies that allow practitioners to create and manage complex IS to solve real-world problems. The main focus of ISE is on building an entire IS architectures that optimize the use of information technology (IT) resources within organizations. An effective IS architecture facilitates the management of information, information repositories, and IS to provide optimized support to management and executives. ISE focuses on the interface, integration, standardization, and interoperability issues of software development.

This journal touches upon all the dimensions related to the ISE field that covers the industrial sectors in addition to educational sectors.

Biomechanics is the application of engineering mechanics (statics, dynamics, strength of materials, and stress analysis) for solutions of biological problems, where it has been pertained to dentistry to study the biologic behavior of oral structures under physical influence of dental restoration.

The main scope of this special issue is to discuss the new methods of computational analysis applied on dental biomechanics fields during loading of an implant, follow up of dental surgeries, treatment of TMJ diseases, biting forces determination, dental image analysis, and test of dental biomaterials.

Research articles, reviews and letters in area of systems and synthetic biology, which involves the use of computational intelligence methods. The purpose of this special issue is to establish fundamentals of computational methods in systems and synthetic biology for enabling further exploration of biotechnology discoveries.

Big Data is a new paradigm for any collection of datasets that cannot be handled by the on-hand data analytics tools and traditional data management systems due to the data characteristics of volume, velocity, variety and veracity. Various data storage and processing systems have been developed based on parallel and distributed computing infrastructure and technologies to support Big Data, such as MapReduce, MongoDB, STORM, etc. The analytics systems that leverage Big Data have also yielded remarkable insights and values to a wide range of applications domains. As estimated by IDC, the Big Data technology and services market is expected to grow from $3.2 billion in 2010 to $16.9 billion in 2015. Therefore, with the fast growing demand of Big Data processing, this special issue will focus on the challenge of developing novel computing and storage infrastructure and parallel analytic tools for Big Data applications.

•Distributed Storage infrastructure:
a.Cloud storage and distributed file system solutions for Big Data
b.Memory-only storage systems design for Big Data
c.Solid-state drive (e.g., flash, PCM) in large-scale storage
d.Performance driven NoSQL database design and query optimization

•Distributed Computing infrastructure:
a.In-memory computing for Big Data
b.Workflow optimization and task scheduling algorithms for Big Data
c.Resource and workload management for Big Data processing framework and systems
d.Performance evaluation and analysis of distributed Big Data processing systems
e.Novel distributed processing framework and system for special types of Big Data, such as graph, multimedia, geographic and streaming data.
f.Parallel data analytic tools and machine learning algorithms on MapReduce-like distributed systems

•Heterogeneous parallel processing:
a.Heterogeneous CPU-GPU solutions for processing Big Data
b.OpenCL and GUDA library optimization for Big Data
c.Parallel data analytic tools and machine learning algorithms on many-core accelerators, such as GPU and Xeno Phi