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IBM System G is a comprehensive set of graph computing software system for IBM Big Data portofolio.

In the Big Data era, data are linked and form large graphs. Traditional IT system was designed for processing independent data. Analyses are mostly done by considering i.i.d. scenario. Processing connected data has been a big challenge.

From the scientific aspect, Network as a new inter-disciplinary scientific field is emerging. Entities -- people, information, societies, nations, devices -- connect to each other and form all kinds of intertwined networks. Researchers from multiple disciplines -- electrical engineering, computer science, sociology, public health, economy, management, politics, laws, arts, physics, math, etc. -- are interacting with each other to build up common grounds of network science. Network theories are being formed for describing the dynamics, behaviors, and structures. A systematic mathematical formalism that enables predictions of network behavior and network interactions is also emerging. Trans-disciplinary approaches are usually required to lay the foundations of this science and to develop the requisite tools. Like 'Computer Science' was coined as an academic disclipine in the 1950s and the first computer science course was taught by IBMers in Columbia University in 1947, we are now envisioning the emerging of 'Network Science' and have been teaching one of the first network science courses since 2010. Graph Computing is the "tool" for Network Science. It is for storing, processing, analyzing, and visualizing connected data.

IBM System G is the first complete software stack for all aspects of Graph Computing. Different combination of System G components can be selected to fit different solution needs. In different use case scenarios, graphs may be large or small, static or dynamic, topological or semantic, properties or bayesian, etc. For instance, small & non-commercial graphs can be stored in open source DB, large-scale graphs can be stored in Hadoop HBase + System G GBase, or highly efficient Native Graph Store. System G is flexibile to allows solutions to pick the components they need while providing common APIs for different layers. These flexibilities are especially suited in the Service and Cloud environment (either private or public cloud).

IBM System G also includes several derived Network Sicence Analytics tools based on the state-of-the-art researches. They include the analytic tools of cognitive networks that are fundametal form of brains -- large-scale Bayesian Network Tools and Deep Learning Tools. We are also developing platform to store, analyze, and visualize mammal brain neurons, with the optimal goal of contributing to the human brain projects. Another aspect of cognitive understanding is to detect and predict human cognition. For instance, understanding people's emotions from their writing, or predicting how text or visual content arouse people's feeling. System G also includes Spatio-Temporal analytics tools to facilitate analysis of moving objects. It also includes a set of behavioral analytics tools, e.g., anomaly detection, recommendations, etc.

System G fills in a gap of the IBM Big Data portofolio. It can help IBM with : 1) Information Management(IM) and Business Analytics, 2) System & Technology (STG), 3) Solutions (SSG), and be potentially used in any IBM Software stack or Service engagement that requires analyzing connected data.

Potential customers please contact your IBM representative for more information about IBM System G. You can engage IBM System G by:

(1) utilizing any of its four types of Graph Computing tools: Graph Database, Analytics, Visualization, and Middleware, or any of the four types of derived Network Science Analytics tools including Cognitive Networks, Cognitive Analytics, Spatio-Temporal Analytics, and Behavioral Analytics to make new applications;

(2) using it through IBM Cloud for your online services; or

(3) deploying any of its existing six business solutions: Enterprise Expertise, Insider Threat, Social Media, Commerce, Healthcare, and Entertainment & Media.