As I was working through updates to the search vendor profiles on Xenky.com, I ran across a reference to Cybertap LLC. The company’s name rang a bell. I recalled an interview one of the goslings conducted with its founder in 2012. The point that caught my attention last week was a reference to a US patent document (8,406,141 B1) that seemed to explain some of the capabilities of “enhanced search.” The title of the patent is “Network Search Methods and Systems.”
If you are not familiar with patent documents, these are available without charge from the US Patent and Trademark Office at www.uspto.gov. The syntax required for the antiquated system is tricky. Please, check the USPTO site for the explanation of how the system processes queries.
The abstract for the invention filed a number of years ago states:
The article How To Do Predictive Analytics with Limited Data from Datameer on Slideshare suggests that Limited Data may replace Big Data in import. The idea of “semi-supervised learning” is presented to handle the difficulties associated with creating predictions based on limited data such as expense and manageability and simply missing key data. The overview states,
“As it turns out, recent research on machine learning techniques has found a way to deal effectively with such situations with a technique called semi-supervised learning. These techniques are often able to leverage the vast amount of related, but unlabeled data to generate accurate models. In this talk, we will give an overview of the most common techniques including co-training regularization. We first explain the principles and underlying assumptions of semi-supervised learning and then show how to implement such methods with Hadoop.”
The presentation summarizes possible approaches to semi-supervised learning and the assumptions it is possible to make about unlabeled data (these include such models as clustering, low density and manifold assumptions). It also covers the concepts of Label Propagation and Nearest Neighbor Join. However, as inviting as it is to forget Big Data, and switch to predictive analytics with Limited Data the suggestion may sound too much like Bayes-Laplace.
The article on PRNewswire titled Attivio and Quant5 Partner to Bring Fast and Reliable Predictive Customer Analytics to the Cloud explains the partnership between the two analytics innovators. Aimed at producing information from data without the hassle of a team of data scientists, the partnership promises to effectively create insights that companies will be able to act on. The partnership responds to the growing frustration some companies face with gleaning useful information from huge amounts of data. The article explains,
‘Over the years I participated in hundreds of workshops and seminars on improvement and innovation. Most of them offered magic outcomes quickly and sustainably. And, Iouri, the real outcome of the majority of these events, for me at least, was a waste of time. Only very few were worth it… Why TRIZ? Is it a new panacea?’ – responded a friend of mine to proposal to come to my TRIZ seminar in Sydney and to discover the Russian ‘silver bullet’.
Abacuses, adding machines and calculators all fulfil a similar function – help us with mathematic operations. Each of these apparatus represents a product that successfully fulfilled the function of its era but eventually was replaced by a superior technology. But why were these products successful? With research suggesting that up to 80 percent of products fail to deliver their intended benefit, it is a question that needs to be answered. One way to ensure a product's success would be to predict a successful product and develop it sooner than your competitors. Sounds unrealistic? It is possible.
One thing is for sure – products do not evolve randomly. There are patterns and tendencies of product evolution that have been identified through the analyses of thousands of patents that commenced in Russia over 50 years ago. The tools of TRIZ are based on these analyses.
TRIZ is the Russian acronym for Cyrillic words which mean Theory of Inventive Problem Solving. TRIZ is a well-established system of tools for innovative problem solving and idea generation. Genrich Altshuller, the ‘father of TRIZ’ started development of the tools in late 1940-es. Some of these tools can be explained as follows:
““The fundamental idea is that topological methods act as a geometric approach to pattern or shape recognition within data,” says a September 2013 article in the journal Science co-authored by Ayasdi CEO Gurjeet Singh. It allows “exploration of the data, without first having to formulate a query or hypothesis.” That is, researchers can find things they did not know they were looking for. For instance, in a database of billions upon billions of phone records scientists could make sense of who was talking to whom.”
Content strategy, at its core, is really easy. It’s all about organizing information in a way that it can be easily searched and retrieved. It’s about labelling files and folders so that they make sense. Val Swisher’s analogy about content strategy being like one’s closet still stands at the heart of it. If you can organize your closet and identify the different clothing pieces in order to categorize them, then you understand how to do content strategy. The only difference is that instead of having shirts, skirts, pants, and shoes to organize, you have folders of documents, webpages, and multimedia. The method of making sure that users can find those documents, webpages, and multimedia should be streamlined, clear, concise, and user-friendly. As content strategists and user advocates, it’s all about making sure that what the audience is viewing looks and reads well, and what the content managers can maintain easily.
Ultimately, when creating a content strategy and setting it up for maintenance, do it correctly now, even if it’s time consuming. If for no other reason, it’ll save time and headaches later. It’s not difficult. It’s just common sense.
