back to article There's a Snowflake in Washington: Microsoft lets data warehouser in on Azure Government

Not content entwining with Oracle, Microsoft has added another third-party database tech to Azure – this time to the Government incarnation of its cloudy platform. The gloriously named Snowflake (at least in the context of the current US administration) is another take on the data warehouse, this time in fully relational ANSI …

  1. I.Geller Bronze badge

    Government users of Microsoft Azure Government can now enjoy Snowflake's relational database smarts over all manner of structured and unstructured data

    How so?

    As is well known and very widely accepted AI technology is my patented Intellectual Property. The essence of technology - in my patented structuring of unstructured data (texts, images, formulas, signs, etc).

    How my database becomes a relational one? The essence in obtaining from texts their synonymous clusters. For example there is a paragraph:

    - Alice, Bob and Ilya laugh merrily. They are happy. Especially cheerful she. --

    In this paragraph there are the following 10 patterns:

    - Alice laughs merrily.

    - Bob laughs merrily.

    - Ilya laughs merrily.

    - They laugh merrily.

    - Alice is happy.

    - Bob's happy.

    - Ilya is happy.

    - Alice especially cheerful.

    - She is happy.

    - She laughs merrily.

    So, in this paragraph there is a cluster, of 5 patterns, characterizing the state of Alice:

    - Alice especially cheerful.

    - She is happy.

    - She laughs merrily.

    - Alice is happy.

    - She especially cheerful.

    This cluster is related in meaning to the cluster derived from this paragraph:

    -- Alice goes in for sports! She laughs merrily and is happy! --

    since both paragraphs have this pattern "Alice laughs merrily."

    Thus AI is a relational database, which contains structured synonymous clusters.

    1. I.Geller Bronze badge

      AI-parsing

      In the fields of computational linguistics and probability, an n-gram is a contiguous sequence of n items from a given sample of text or speech.

      My patented AI-parsing works for a discontinuous sequence of n elements as well:

      United States Patent 8,504,580

      "3. The method of claim 1 wherein each context phrase is a combination of a noun with other parts of speech at least one of which is a verb or an adjective."

      N-gram parsing was the only one for the past 75 years, my the first novelty in the field because it works for a discontinuous sequence of n elements as well.

  2. I.Geller Bronze badge

    A guess

    I think Microsoft uses AI database...

    "The algorithm, called Space Partition Tree And Graph (SPTAG), allows users to take advantage of the intelligence from deep learning models to search through billions of pieces of information, called vectors, in milliseconds. That, in turn, means they can more quickly deliver more relevant results to users.

    Vector search makes it easier to search by concept rather than keyword. For example, if a user types in “How tall is the tower in Paris?” Bing can return a natural language result telling the user the Eiffel Tower is 1,063 feet, even though the word “Eiffel” never appeared in the search query and the word “tall” never appears in the result."

    Microsoft uses the name and nouns Paris + tall + tower creating clusters around them, expands the query and finds "Eiffel Tower is 1,063 feet".

    1. I.Geller Bronze badge

      Dictionary

      How?

      The word "tall" has these definitions:

      - adjective

      --- of great or more than average height, especially (with reference to an object) relative to width.

      "a tall, broad-shouldered man"

      synonyms: big, high, large, huge, towering; More

      --- (after a measurement and in questions) measuring a specified distance from top to bottom.

      "he was over six feet tall"

      synonyms: in height, high, from head to toe/foot

      "he's about 5 foot 8 inches tall"

      Microsoft, having a structured into the synonymous clusters text on Paris towers (in its AI database), can chose one of these 2 definitions.

      How?

      The word "height" has its own definitions, which are to be added to one of the definitions for the word "tall", and sieved (as a structured texts) through the anchor text on "towers". The same should be done for the rest of definitions' words.

      As the result Microsoft gets uniquely indexed AI database, which was the target of NIST TEC QA, and can make an answer in the best its traditions!

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