Microsoft Data Insights Summit Videos Published — by Chris Web’s Blog

The Microsoft Data Insights Summit finished today, and the videos of the sessions have already (!!) been published online here:

via Microsoft Data Insights Summit Videos Published — Chris Webb’s BI Blog

Ορολογία της Ημέρας (T.O.D) – 26/6/2017 [Enterprise Search]

Enterprise search is the organized retrieval of structured and unstructured data within an organization. Properly implemented, enterprise search creates an easily navigated interface for entering, categorizing and retrieving data securely, in compliance with security and data retention regulations. An organization may make search available to clients and business partners as well as employees.

The quality of enterprise search results is reliant upon the description of the data by the metadata. Effective metadata for a presentation, for example, should describe what the presentation contains, who it was presented to, and what it might be useful for. Given the right metadata, a user should be able to find the presentation through search using relevant keywords.

There are a number of kinds of enterprise search, including local installations, hosted versions, and search appliances, sometimes called “search in a box.” Each has relative advantages and disadvantages. Local installations allow customization but require that an organization has the financial or personnel resources to continually maintain and upgrade the investment. Hosted search outsources those functions but requires considerable trust and reliance on an external vendor. Search appliances, the least expensive option, may offer no customization at all.

Enterprise search software has increasingly turned to a faceted approach. Faceted search allows all of the data in a system to be reduced to a series of drop down menus, each narrowing down the total number of results, which allows users to narrow a search to gradually finer and finer criteria. The faceted approach improves upon the keyword search many users might think of (the Google model) and the structured browse model (the early Yahoo model). In the case of keyword search, if the end user doesn’t enter the correct keyword or if records weren’t added in a way that considers what end users might be looking for, a searcher may struggle to find the data. Similarly, in a browsing model, unless the taxonomies created by the catalogers of an enterprise’s information make intuitive sense to an end user, ferreting out the required data will be a challenge.

Enterprise search is complex. Issues of security, compliance and data classification can generally only be addressed by a trained knowledge retrieval expert. That complexity is further complicated by the complexity of an enterprise itself, with the potential for multiple offices, systems, content types, time zones, data pools and so on. Tying all of those systems together in a way that enables useful information retrieval requires careful preparation and forethought.

Vendors of enterprise search products include Oracle, SAP, IBM, Google and Microsoft.

Many Thanks to TechTarget for its incredible content.

[GRUsersOfBIApps Admin]

Ορολογία της Ημέρας (T.O.D) – 29/1/2017 [Machine Learning]

Machine learning is an area of computer science and statistical modeling that allows a computer program to predict an outcome or make a decision without being explicitly programmed to do so.

Machine learning, which forms the basis for artificial intelligence (AI), is closely tied to data analytics and data mining programming. Continue reading

Matibabu – Νέες Εξελίξεις στην Υγεία – ενάντια στην Μαλάρια

Matibabu, which is competing in our Hardware Battlefield at CES today, isn’t looking to cure any diseases. Instead, the Uganda-based company is looking to make it easier to diagnose malaria so those who are infected can get the right help faster. I actually first ran into Matibabu back in 2013 when the team competed in…

via Matibabu uses light to diagnose malaria — TechCrunch

Analytics company Heap raises $11M — TechCrunch

Heap announced today that it has raised $11 million in Series A funding. We’ve written about the company’s “capture everything” approach to analytics before — it aims to collect data about every tap, swipe and other action that a user takes on a website or app. CEO Matin Movassate said this should allow anyone at…

via Analytics company Heap raises $11M — TechCrunch

Ορολογία της Ημέρας (T.O.D) – 12/06/2016 [Analytics Platform]

Οι Αναλυτικές Πλατφόρμες γεννήθηκαν ως ένας συνδυασμός πολλών και διαφορετικών “εργαλείων” της πληροφορικής επιστήμης, (ένας συνδυασμός υλοποιήσεων – hardware και software) που καθαρά άποψης software, περιλαμβάνουν ποικίλα εργαλεία, που εκτελούν ένα μεγάλο αριθμό υπολογιστικών εργασιών. Τέτοιες εργασίες μπορεί να είναι: 1) Στατιστικές Αναλύσεις, 2) Μαθηματικοί Υπολογισμοί, 3) Στατιστικός & Μαθηματικός Διαφορισμός, 4) Αναλύσεις Κειμένων, 5) Εξόρυξη Δεδομένων από πηγές δεδομένων (δομημένες ή αδόμητες), 6) Αποθήκευση δεδομένων, 7) Συντήρηση δεδομένων, 8) Ανανέωση δεδομένων κλπ άλλες εργασίες που αφορούν τα πεδία της υπολογιστικής επιστήμης που ονομάζονται “machine learning”, “computer assisted learning”, natural language processing” και άλλες.

Ένας ορισμός στην Αγγλική γλώσσα της έννοιας Αναλυτική Πλατφόρμα, παρατίθεται κατωτέρω:

Continue reading