One of the biggest challenges of enterprise search is the variety and number of sources where data resides. Managing all those sources can be a nightmare. Epinomy provides a point & click dashboard that makes ingesting data from almost any source a breeze. Structured, unstructured, web feeds, document folders, data in other repositories…ingest it or connect to a source where the data resides elsewhere using the Data Source Registry built into Epinomy.
Features
List of Feeds
The Data Source Registry displays a comprehensive list of all the sources of data searchable in Epinomy. Click on one of the sources to manage it in Epinomy.
File Systems
Windows
Unix/Linux
Mac OS-X
Databases
ODBC
JDBC
CSV Files
XLS Files
Other Database Files
Sites and Feeds
Web Sites
RSS Feeds
Drupal
Content Management
SharePoint
LiveLink
Exchange
Connect
Connecting a source to Epinomy simple. Define the source and where it lives and Epinomy does the work. Epinomy can connect to multiple data sources and ingest multiple data formats into it's three fundamental internal data structures: tables, text and triples.Tables - Tables are structured data sets, particularly time series. Tables contain the structured data in an enterprise, including:
Time Series
Financial Data
System Logs
Telematics
Signals
Internet of Things
Purchase Behavior
Customer Metrics
Query History
Text - Text is unstructured data in the form of documents. Some examples of text includes:
E-Mail
Office Documents
Word
Excel
Powerpoint
PDF
XML Documents
Triples - Semantic triples are the best way to represent linked structured data including:
RDF
Vocabularies
SKOS (Simple Knowledge Organization System)
Taxonomies
Ontologies
Linked Open Data
Concepts
Social Graphs
Site Maps
Load
Once Epinomy has connected to the source, describe what you want loaded and how you want it loaded.Transform
Epinomy transforms the data from individual sources by running any number of processes that will format the data being ingested from that source that will define how that data interacts with other feeds and performs in search.
Some examples of pre-configured pipeline stages include:- Dimensional Analyser - extracts concepts from structured data to be stored in the triple store
- Element Mapper - Maps input elements and columns to normalized internal format.
- Disambiguator - Automatically creates links among similar concepts.
Schedule
The scheduler permits periodic polling of data sources for new information. Depending on the currency of the data source, periodicities can vary from real-time to one-time.