We are surrounded by isolated silos of data and information. The barriers to merging distinct sources of data and information are high. It takes too much time and effort to reconcile the differences among these sources. Mosi Systems has the tools, methods and experience to help you and your organization overcome these challenges. We can help you break down those data silos for unique insight and foresight.
Mission: We share a vision is that data and information are the basis for effective operations, competitive differentiation and execution on the enterprise's strategy. Today there are too many silos and too many disconnects among operational teams to fully unleash the enterprise's full potential.
Place your orders, this robot has the semantic smarts to bring you a sandwich! Does it reconcile hoagie, hero and sub with "sandwich."
Scientists have put semantic search technology into motion, literally.
Three Strategies to Prepare Your Business for Big Data
Companies need to focus on these three key strategies to prepare themselves to benefit from big data.
Three Strategies to Prepare Your Business for Big Data
Is your company ready to adapt to how Big Data and the Semantic Web are going to change the practice of competitive intelligence and marketing research?
Social networks, big data and the semantic web are changing the practices of competitive intelligence and marketing research. These two professional practices c
The cofounders of Mosi Systems have proposed the session "Predicting the Future of the Semantic Web" for the South by Southwest Interactive 2012 conference. Please go over to the SxSW Panel Picker site and vote for our panel.
When do you create a subclass and when do you use properties in designing your ontology? In this blog entry I explain why this decision is important and share our experience.
When should you reflect distinctions through a subclass and when to reflect those distinctions as properties i your ontology design?
In this blog post we geek out about designing ontologies. Specifically, what does an ontologist need to do when they are designing an ontology that will be transformed into a relational database?
Modify your approach when designing an ontology that is intended to function as a relational database.
Is there a deeper meaning behing the name "Mosi Systems?" Why, yes, there is!
The name "Mosi" refers to a natural wonder that is also the perfect metaphor for enterprise data.
The McKinsey Global Institute recently published an extensive report on the implications and benefits of what they call Big Data. McKinsey highlights some of the trends contributing to the growth both in volume and impact of data. The report also attempts to quantify the economic benefits of data analytics.
Some industries will see greater benefit from analysis of big data.
The trends towards standardization of software, consolidation among IT vendors, software as a service and cloud computing suggest to some that the ability of IT to establish competitive advantage is diminishing. However, we know that effective application of IT to business processes, enterprise data management and operations remain a basis for competitive differentiation.
IT is becoming critical to competitive differentiation and operational effectiveness. The CIO is becoming an enterprise strategist.
Semantic success requires certain competencies be ingrained in the “muscle memory” of the enterprise. Here are four practices that develop those competencies.
The basis for operational and competitive advantage will narrow as standard semantic search packages become the norm.
To be ready for the semantic future, you should make sure your enterprise search team is familiar with the other initiatives with which search is likely to converge. Move beyond simple technical requirements in your discussions with stakeholders to better understand the organization, its objectives and the larger industry or context in which it operates.
As enterprise search incorporates semantic technologies the teams that manage these projects need to become smarter about the enterprise.
Anybody who has ever had to pull data from multiple sources for the purpose of analysis knows how difficult it can be to get this done. With ontology, these adjustments can be fully-defined and instantiated into automated tools that can handle real-time, high-volume data flows and easily adapt to changes in the underlying data sources, systems and processes.
Enterprises will be able to automate analysis that pulls from multiple data sets using ontology.
The discussion that has arisen from our last blog entry on information agents reminds me of another vision of the semantically-enabled future from the late 1980s. This vision is Apple's Knowledge Navigator videos. Probably the best-known of these is one in which a university professor interacts with a software agent. The video is a brief, fun diversion.
In the late 1980s Apple created a series of videos that hint at the ontology-enabled semantic web future.
I get my most excited when I think about how ontology will underpin automated agents that help us manage our flows of information, protect our personal data security and, on some basis, take specific action in response to events on our behalf.
A vision for the semantic web future in which automated information agents use ontology to help us make information overload a thing of the past.
As I’ve tested elevator pitches it’s become clear to me that the word “ontology” is the kiss of death with a few audiences. Friends and family without a background in IT immediately assume it has something to do with being a cancer doctor. Often those who are familiar with ontology have been burned by academic exercises and are very sceptical that ontology has anything practical to offer.
The first in a series of blog entries on a future vision for agile enterprises enabled by semantic web technologies, especially ontology.
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