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e-Infrastructure Integration-with gCube
- 1. e-Infrastructure Integration with gCube Andrea Manzi ( CERN ) Pasquale Pagano ( ISTI-CNR ) EGI User Forum 13 April 2011 Vilnius ( Lithuania ) www.d4science.eu
- 4. gCube architecture gCube run-time environment gCube Definition and Management Services gCube Application Services Presentation Services Portlets Application Support Layer Information Organization Services Storage Management Collection - Content - Metadata - Annotation -… Management Information Access Services Search Framework Ontology Management Personalization Service Index Management Framework DIR Support Framework Process Execution Management VRE Management Information System Security gCube Container gCore Framework User Services
- 25. Questions Time
Notes de l'éditeur
- Scenarios: TS curation 2? 3) the Aquamaps maps expert validation
- VRE resources can be published in the VO at any time by the VRE data managers.
- Interoperability is among the most critical issues to be faced when building systems as "collections" of independently developed constituents (systems on its own) that should co-operate and rely on each other to accomplish larger tasks. Unfortunately, interoperability is a kind of problem that has multiple facets and it is very challenging. Interoperability issues arising whenever two (or more systems) are willing to ''share'' a certain resource (whatever it is) and one of the two systems plays the role of ''provider'' of the resource while the other plays the role of a ''consumer'' of this resource. The multiple facets result from the fact there are multiple barriers hindering the involved systems to ''share'' a common understanding of the resource that is the target of the interoperability scenario. These barriers range from different models of the resource to different protocols and API to access the resource and interact with it, different policies (and policy models) governing the resource consumption, etc.
- Different Interoperability approaches
- Resources accessble from a common ecosystem of resources and different communities can access different Ecosystem views. Communities define their VREs ( trasparentrly from the providers, which could be also Cloud systems) Competition of the same resources btw VREs ( eg. Indexes or Storage)
- Blacboard bases ( Information System) Wrapper /medoator based ( CM) Adaptor-based ( PES adaptor over condor, grid and hadoop) map reduce)
- OCMA is an open, WSRF-compliant architecture for gCube content management services. OCMA defines a design pattern for such services and, by contextualisation of the pattern, their role in a gCube infrastructure. Requirements and Assumptions OCMA acknowledges that gCube is concerned with content that may: be hosted inside or outside a gCube infrastructure; be described with a variety of models, for different media, and with different degrees of structure; be accessed with a variety of protocols; OCMA makes only the following assumptions about content: content is created, accessed, and distributed in units called documents ; documents are grouped in collections ; collections are hosted in local management systems called repositories . Finally, OCMA acknowledges that content management in gCube needs to: embrace heterogeneity , i.e. support simultaneously multiple locations, protocols, and models; hide heterogeneity , i.e. abstract over differences in location, protocol, and model; scale , i.e. retain good throughput under heavy load;
- OCMA is the Architecture, flagship CM sevice A storage back-end R may already offer a native T-interface. In this case OCMA relies on wrapper for R . A storage back-end R may offer a different T'-interface. In this case OCMA relies on adapter for R .
- Composition of: Data access to External infrastructures ( Aquamaps) Data process on Hadoop Data process on glite
- In statistics , signal processing , econometrics and mathematical finance , a time series is a sequence of data points , measured typically at successive times spaced at uniform time intervals. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
- R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.