Analyses and Sense Making
- Semantic Federation of Databases - SemFed
- eXtensible Knowledge Server - XKS
- SPARQL XTS – the XKS-based Triple Store
Our “brain in a box” also provides solutions to these IT challenges:
There’s no need to pay more for your database software just because you increased the number of cores in your servers. Talk to us about how we can provide a single priced XKS that also gives you powerful analytics.
Our Reasoner can supply you with sophisticated Role-Based Access Control, protections for Personally Identifiable Information (PII), and Fact-Level Reasoning on Security Labels and Security Policy. Talk to us about your secure data access needs.
There are broadly two kinds of big data problems. 1. Lots of data with a high noise to signal ratio, e.g. social media data, in which the challenge is filtering out masses of noise and finding useful data and discerning its meaning in the context of your problems; and 2. A far more difficult technical challenge is one of lots of data that is deeply and complexly interrelated and might exist in many repositories. In #2 the technical challenge is modeling the meaning inherent but not overtly present and instantiating this in an analytic system. HIGHFLEET solves both Big Data problems. Talk to us about your Big Data needs.
Data Warehouses can be brittle and expensive to modify if you want to do new analytics or meet new regulatory challenges. And can be rendered useless if they cannot keep up with secure data access issues. HIGHFLEET can put our Reasoner system in front of your Data Warehouse to provide analytic flexibility and responsiveness to secure data access and other issues. Talk to us about your Data Warehouse needs.
As with fluidOps and BrightPlanet HIGHFLEET can enhance your products and solutions by embedding our capabilities as with fluidOps, or via joint solutions, as with BrightPlanet. Talk to us about working with HIGHFLEET.
Semantic Federation of Legacy Databases
Analytics and Sense Making across all enterprise databases. HIGHFLEET's Semantic Federation Solution gives the enterprise a cost effective way to get full value from its separate information repositories.
HIGHFLEET's solution for federating existing databases uses our tools, software and professional services to semantically federate small or large numbers of separate enterprise databases. Our solution keeps the cost curve flat for database federation. And we give a superior result, providing the end user knowledge discovery across all federated resources as if they were one coherent deductive system. The pace of operations and the value of understanding the relationships between enterprise information require HIGHFLEET's Semantic Federation Solution.
What you get:
HIGHFLEET’s Semantic Federation of Existing Databases (SemFed)
- Analytics and Sense Making - As in all our solutions, HIGHFLEET’s analytic capabilities are “baked in”. Query in our systems performs analysis not just data retrieval. In HF’s systems a user can ask “about something” not just “for something”. And our systems can be easily integrated with existing applications. SemFed connects enterprise data across resources at the level of meaning. Stop drowning in data.
- Lower Implementation Costs - Far less expensive than traditional approaches - we automate a good portion of the federation process. And HIGHFLEET’s experience, pre-existing complex models (ontologies) and tools also contribute to lower implementation costs.
- No Disruption to the Enterprise - We can “change the engines while the plane is in flight”. We can leave local databases in place, never disturbing daily operations. This also removes a cultural barrier to implementation.
- No need for Business Process Re-Engineering, or what one CEO called, “The wet cement of enterprise management software.”
- Rapid benefit - Far less time to value - see #2.
- Flexibility - Applications do not break if the logic model/ontology is extended or schemas change in the federated databases.
- No need for a data warehouse. Data warehouse projects are often expensive failures. See these links http://www.dwinfocenter.org/against.html and http://www.tdan.com/view-articles/4876/ Data warehouses are also very rigid. If a user wants a different kind of analysis, it is very hard to re-do the warehouse to provide this. Changes in HF can take a few hours to a couple days, rather than weeks or months.
- Data Cleansing - Our methods reveal previously existing bad data. We use variations on our capabilities to speed the process of data cleansing. Also, since our systems use logic and a Reasoner the system’s “Integrity Constraints” will flag data entries that violate the logic in the system, preventing data corruption. See http://www.mgharney.com/inspiration_002.htm
“It was estimated by the Data Warehousing Institute that it costs US business over $600 billion a year on bad data.”
The XKS Family of Knowledge Servers
If you need to retrieve data, get a database. If you need knowledge discovery, analytics and sense making, get HIGHFLEET's XKS™.
The XKS provides powerful analytics “baked in”. As a deductive database, the XKS can perform very complex analysis simply via query.
- XKS – The base XKS is sized for 20,000,000 assertions, but can be sized much larger to billions of assertions.
For more information, please contact us.
- Maximum Flexibility for All Applications - The XKS™ gives unprecedented flexibility for our customers, since a variety of relational data stores can be employed to suit project requirements for performance, scale and portability -- and we can take advantage of the customer’s existing database licensing.
- Ease of Use – Ask About Something, not just For Something - Thanks to its inherent high-fidelity model and extensive inference capability, users new to the system or new to the domain can make useful queries without extensive training. You don't have to know exactly where you're going to get started.
- Hand Held Devices to Enterprise Systems - HIGHFLEET’s XKS™ Family can be used for every application from hand held devices to large-scale enterprise systems with large data sets and many simultaneous queries.
- Scalable - Our XKS™ model also permits a Virtual Knowledge Server Architecture whereby multiple XKS instances all utilize the same persistence store, giving extremely high rate query access.
- The XKS™ Functional Components
- the Server Presentation Layer, our API interface,
- the Semantic Query Optimizer assures excellent performance on complex query,
- our Parallel Query Engine (PiQuE) provides efficient analysis using optimized massively parallel query processing,
- Persistence Store type is based on customer need.
SPARQL XTS (eXtensible Triple Store)
While not nearly as capable as our XKS, the XTS is designed for those clients who have made a significant commitment to triple stores and want to maximize the value of that investment. Our XTS is more capable than other triple stores – see below.
The XTS is a triple store built on the foundation of HIGHFLEET’s XKS technology. While all triple stores support RDF data management and a smaller number support RDFS or OWL inference, the XTS provides a combination of capabilities not found in other triple store systems:
- Transactions – The ability to update and query RDF data under transaction control prevents incorrect query results and corruption of data. Queries and updates initiated by separate processes are kept separate, enhancing the usefulness of the store for large-scale enterprise applications. This feature is also useful for performing “what if” style analytics. Data may be added, analyzed, and removed, all within a single transaction.
- On-demand inference – Typical triple stores perform inference en masse when data is loaded. While this makes for very efficient queries once the (often time-consuming) process is complete, it introduces latency into data loading and greatly amplifies storage requirements over that needed for the base data. The XTS performs inference on-demand when a query is issued. This means that only the work required to answer the query is performed. Caching of query results insures that work done to answer a query is amortized over the application’s lifespan.
- User-defined inferences – Some applications benefit from inference rules in addition to those supported by RDFS and OWL. By allowing user to specify their own inference rules, in addition to or in place of standard RDFS or OWL inference rules, users have more control over application function and performance.