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In computing, Oracle Coherence (originally Tangosol Coherence) is a Java-based distributed cache and in-memory data grid.It is claimed to be intended for systems that require high availability, high scalability and low latency, particularly in cases when traditional relational database management systems provide insufficient throughput, or insufficient performance.
Free availability under the GNU General Public License. Portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform. A comprehensive collection of data preprocessing and modeling techniques. Ease of use due to its graphical user interfaces.
Free Grid MP: Univa (formerly United Devices) Job Scheduler no active development Distributed master/worker HTC/HPC Proprietary: Windows, Linux, Mac OS X, Solaris Cost Apache Mesos: Apache actively developed Apache license v2.0 Linux Free Yes Moab Cluster Suite: Adaptive Computing Job Scheduler actively developed HPC Proprietary
Solr (pronounced "solar") is an open-source enterprise-search platform, written in Java.Its major features include full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features [2] and rich document (e.g., Word, PDF) handling.
Apache Cassandra is a free and open-source database management system designed to handle large volumes of data across multiple commodity servers.The system prioritizes availability and scalability over consistency, making it particularly suited for systems with high write throughput requirements due to its LSM tree indexing storage layer. [2]
Free and open source. High-availability cluster. Apache Mesos, from the Apache Software Foundation; Kubernetes, founded by Google Inc, ...
Solaris Cluster is an example of kernel-level clustering software. Some of the processes it runs are normal system processes on the systems it operates on, but it does have some special access to operating system or kernel functions in the host systems.
In contrast to the k-means algorithm, k-medoids chooses actual data points as centers (medoids or exemplars), and thereby allows for greater interpretability of the cluster centers than in k-means, where the center of a cluster is not necessarily one of the input data points (it is the average between the points in the cluster).