The uRiKA graph appliance fills an unmet need in the rapidly-growing Big Data market. While many critical Big Data problems are based on graphs, most current Big Data solutions are based on partitioned data structures that scale out on clusters. Current Big Data approaches, including graph databases, result in low performance on graphs since graphs are hard to partition across cluster nodes, are non-deterministic, and are highly dynamic. The launch of the uRiKA solution addresses the challenge of delivering insightful analytics on graphs, not only in terms of its ability to handle size and complexity of relationships, but also in terms of its response time and speed of processing.
"Graphs are an important segment of the Big Data market with increasingly important applicability to problems in areas such as social networking, healthcare, finance, life sciences, and telecommunications", stated Tony Baer, Principal Analyst, Ovum Research. "Given the importance of real-time interactive analytics on graphs, it's not just about Big Data but also Fast Data."
The uRiKA graph appliance is specifically designed for a graph-based approach to Big Data analytics. By bringing together data and relationships from multiple sources, and enhancing these relationships through automated inference and deduction, the uRiKA graph appliance builds up a relationship warehouse. The system supports both real-time visualization of relationships for interactive discovery and real-time searches for relationships based on partially-specified patterns and templates.
"With the launch of the uRiKA graph appliance, YarcData now enables data scientists to quickly get to what we call the uRiKA moment - the ability to discover unknown, unforeseen and hidden relationships in Big Data", stated Arvind Parthasarathi, General Manager, YarcData. "By combining high-performance, graph optimized hardware with an industry-standard, open-source software stack, YarcData's uRiKA graph appliance enables enterprises to have a rapid time to value on their Big Data relationship analytics initiatives."
The uRiKA graph appliance is available now and is successfully deployed at multiple customer sites. Early adopters include the Institute of Systems Biology (ISB), Mayo Clinic, Noblis, Swiss CSCS and a US government organisation.
Life Sciences uRiKA Customer Use Case: "Data-intensive graph problems abound in the Life Science drug discovery and development process", stated Leroy Hood, President of ISB. "uRiKA enables ISB's research efforts on the Cancer Genome Atlas by discovering unforeseen relationships across a graph of multiple types of cancers, datasets of multiple data types, such as unstructured, text, sequence, numerical, images, etc. and all of these studied by different platform technologies. uRiKA's support for open standards like Java, RDF and SPARQL makes ISB's work extensible, feasible and sustainable, reduces our analysis time and makes possible new approaches to generating disease models that are both predictive and actionable (can help patients)."
Healthcare Provider uRiKA Customer Use Case: With the rich medical history data set of ten million patients, physicians want to identify "similar" patients to ensure consistent selection of the most effective treatment. Since each physician defines "similarity" differently for each patient based on a multitude of parameters including events, symptoms, diagnoses, diseases, treatments, prescriptions, genetics and family history, uRiKA enables real-time, interactive analytics based on physician-specified ad-hoc patterns on the entire patient relationship graph.
Features and benefits of the YarcData uRiKA graph appliance include: