Bridging people and data with unified information search, access and analytics.
The CloudView indexing module uses statistical, linguistic and semantic processing to transform heterogeneous, multi-source data flowing from the Connectivity and Semantic Factory modules into a unified base of actionable information intelligence.
The CloudView index stores and maintains this data and processes incoming user and application queries against it. The module further performs qualitative and quantitative analytics on textual, numerical, temporal and geographical values, both during indexation and on-the-fly at query time.
It is also a secure, non-intrusive platform that scales linearly and massively at a low hardware cost, adapts easily to changes in source data, and supports flexible query input/output strategies adapted to your business rules and priorities.
Empowers users to become self-reliant decision-makers
Integrates intelligence from all relevant sources
Guarantees secure enforcement of source-system rights
Supports both qualitative and quantitative analytics with a single platform
Scales linearly at a low hardware cost
CloudView Index Highlights
Low latency indexing whatever the volume of data
Sub-second semantic query processing regardless of index size
Massively-scalable faceting (categorization and clustering) of textual, numerical, data and geographical values
Query-time definition of virtual fields (e.g., computing Sales on Unit Sold x Unit Price)
Dynamic aggregation of facet/field values using functions such as avg, centile, max, min, stddev, sum, etc., to produce, for example, Revenue by Region, Brand, Customer, etc.
Auto-partitioning of numerical values (e.g., under $10, $10 to $100, over $100)
Dynamic pivot-style tables that join facets along multiple axes (e.g., Sales, Region, Date, Products, Vendors…)
Scheduled alerting is complemented by real-time alerting, with notifications triggered as soon as information matching stored queries are indexed. Flexible standard and custom options are available for publishing notifications: email, Web services, RSS, etc.
Deep user context for maximum relevancy: integration of contextual data like geolocation, easy generation of role- and rule-based views, incorporation of user ratings and tags, etc.
User assistance through semantic interpretation of natural language queries, fuzzy matching of results, faceted navigation, query suggestions and much more
Diagnostic tool improves the user experience by helping administrators more easily detect and respond to configuration issues
Massively-scalable faceting for dynamic analytics: above, sentiment analysis facets represented on a heat map; below, geographical faceting.