Social Engagement Analytics at MicrosoftSocial: In a socially connected world, engagement with customers can happen anywhere, anytime, and it is key to growing your business. Microsoft Social Engagement puts powerful social tools in the hands of your sales, marketing, and service teams—helping them to gain insight into how people feel about your business and to proactively connect on social media with customers, fans, and critics. Take a guided tour

Social listening, Take your social pulse: Microsoft Social Engagement helps you harness the power of the social web by analyzing what people are saying on social media and in the news. We support 19 languages and cover a broad range of sources—Twitter, Facebook, videos, blogs, and news syndication. Spot trends and receive alerts about how people are feeling about your products or brand so that you can shape your messaging and sales conversations more effectively. You can identify your key influencers. Read more about Microsoft Social Engagement

Social analytics, See social from any angle: The robust social analytics of Microsoft Social Engagement give you more insightful and interactive analytics with richer data to gain a true understanding of your business on topics that matter most. Our visual filter allows you segment your data by source, sentiment, location, or author. Our unique sentiment monitoring combines NLP and ML to evaluate keywords in posts to determine positive, negative, or neutral. Learn about turning social sentiment into opportunity

Social engagement : Connect with your customers. Empower teams across sales, marketing, and service to collaborate with customizable streams from a combination of sources. Build deeper relationships with customers by empowering them to engage with social communities on Facebook and Twitter.

Social CRM: Get social in Microsoft Dynamics CRM. You can add social data in Microsoft Dynamics CRM or Microsoft Dynamics Marketing—on dashboards or on any forms, like Accounts or Campaigns. Integrate social interactions into an end-to-end customer journey, with the ability to create leads, opportunities, or cases from social posts, right within Microsoft Dynamics CRM Online. Help your front-line sales, marketing, and service teams deliver amazing customer experiences. Plus, social engagement doesn't have to be limited to a few individuals in your marketing organization. Take a guided tour

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Customers are transforming their business with Microsoft Social Engagement: Marston's gets social with Microsoft Social EngagementTo meet the company's need to monitor social media, to recognize, follow, and analyze trends,  and to then quickly share the relevant insights with the right people to swiftly react and meet the customers' needs, Marston's chose Microsoft Dynamics CRM and Microsoft Social Engagement."What's important for us is not just to know the quantities of what people are talking about, but the nature of what they are talking about—and there's such a mass of information that we need to be able to boil it down to things that we can then act upon."  Beck Johnson, Marston's Pubs      Read the Marston's Pubs case studyRead more customer stories

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T1P1

1000

00100000

00000001

T1P20

1000

01000000

00100000

T3P111000

10000000

01000000

T3P1

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00010000

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T3P17

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10000000

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T4P8

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T10P111000

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00000001

T10P111000

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00100000

T10P111000

00000010

00000100

T10P110100

10000000

10000000

T10P110100

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00010000

T10P110100

00100000

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T10P110100

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T10P110100

00000100

00001000

T10P110100

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00000001

T10P110010

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00100000

T10P110010

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00010000

10000000

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00010000

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T10P110001

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T10P110001

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10000000

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00010000

T10P10121OfThese

T10P14116OfThese

T10=$IDT1P1515OfThese

T11P12

1000

10000000

00100000

T12P3120OfThese

T12P9120OfThese

T12=$HIVEP13120OfThese

T13P6120OfThese

T13P14120OfThese

T13=$GLUUP23120OfThese

T14P111000

00100000

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T15P1121OfThese

T16P6120OfThese

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T18P4

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T19=$AAPLP=most

273 of these

T=20P=3

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T=251 =buyP=4

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10000000

T=252 =BuyP=3

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01000000

Simple Sentiment Analysis strategy 1: Using the map pTree, P=DPbuy,4 OR Dpbuy,8 buy all stocks mentioned to buy in any document which has a 1 in P.P has 1 for doc=25 and doc=2Doc2: $VHC reiterated. Buy and 65 target @Gilford. Expect $AAPL trial to proceed and confident that $VHC will win.Doc25: C. Hallum reit buy on $SIMO Phison said control unit sales would double which is + industry. Was down on $AAPL suing Android industrySo the strategy would conclude: Buy $VHC and $SIMO.

Of course, this is much to simple a strategy. The main point is that such text mining is the perfect area to demonstrate the facility of multilevel pTrees.For the OR above, all 3 levels of the two pTrees could be concatenated and ORed in one operation (instantaneously).The result would be P=10001001000011000000. Then we interpret: The 1st 4 bits are level=2, which tells us the only possible 1bits are in the first stride (docs 1-64). The next 8 tell us the only 1bits are in docs 1-8 or docs 25-32.The final 8 bits tell us the docs with 1 bits are 2 (the one within 1-8) and 25 (the one within 25-32).

What if there are 1 trillion = 240 Documents?Stride= 64=26, we’d have 7 concatenated levels pTrees of 448 bitsStride= 128=27, have 6 levels and Concatenated_pTree_Length = 786 bits

Stride= 32=25, have 8 levels and Concatenated_pTree_Length = 256 bitsStride= 16=24, have 10 levels and Concatenated_pTree_Length = 160 bitsStride= 8=23, have 14 levels and Concatenated_pTree_Length = 112 bitsStride= 4=22, have 20 levels and Concatenated_pTree_Length = 40 bitsStride= 2=21, have 40 levels and Concatenated_pTree_Length = 40 bits

Where’s the sweet spot? I like stride=214. Recall # of levels dictates the # of separate interpretation steps (each involving comparisons and IfThenElses…). This comparison assumes a single 1bit in each pTree (true for PpTrees and TpTrees but not DpTrees – witness DpTree$HIVE,2 with 20 1 bits). Also, 1 counting speed is a critical consideration.

DpTrees are the only pTrees w multiple 1bits. TpTrees and PpTrees always have<=1 1bits. The depth of a TpTree probably maxes out at 100,000 and a PpTree depth depends on the nature of the document corpus (for tweets, ~256?, for email, ~1000?, for books, 500,0000?, for webpages ~500?). The point is, since TpTrees and PpTrees are relatively so small, does it pay to use multilevel there at all? (e.g., ANDing and Oring 500,000 bit vectors is pretty fast and then there would be no interpretation of segments to do.

Stride= 256=28 have 5 levels and Concatenated_pTree_Length = 1,280 bitsStride= 512=29 have 5 levels and Concatenated_pTree_Length = 1,280 bitsStride= 1024=210 have 4 levels & Concatenated_pTree_Length = 4,096 bitsStride= 2048=211 have 4 levels & Concatenated_pTree_Length = 8,192 bitsStride= 4096=212 have 4 levels & Concatenated_pTreeLength = 16,384 bitsStride= 8192=213 have 4 levels & Concatenated_pTree_Length = 32,768 bitsStride=16384=214 have 3 levels & Concatenated_pTree_Length = 49,152 bitsStride=32768=215 have 3 levels & Concatenated_pTree_Length = 98,384 bitsStride= 1,048,576=220 2 level and the Concat_pTree_Length = 2,097,152 bits

Stride= 1,099,511,627,776=240 1 level and the Length = 1,099,511,627,776 bits

Stock Market PredictionCompose a historical TB2H (TooBigToBeHorizontal) TrainingSet,

TB2H(Time, Stock, WallStreetMeasurement1,.., WSMw, MainStreetMeasurement1,…,MSMm, Class)

which contains a column for every pertinent Wall Street and Main Street input (e.g., Russia invades Kiev!) on each stock on each historical Time period from the “current” time period (e.g., Time Unit might be days, hours, microseconds, …). So WSMkCurrentTimeUnit, WSMkOneTimeUnitAgo, …

The distance function would typically be weighted lower the more aged the input (e.g., by 1/(1+exp(DaysAgo)) and weighted zero for future inputs.And distance( ($AAPL,Today), ($GOOG, 100DaysAgo) ) would be the weighted distance backwards from Today for $AAPL matched with weighted distance backwards from 100DaysAgo for $GOOG.

Note: For $AAPL, e.g., there is a record for each succeeding day is widened by w+m new columns

The Class Column might be: StockPrice increased/decreased (by a threshold % during that Time period?).

Strategy1:Build a Hulls around the “increased” and “decreased’ classes. Classify each stock each new Time period. Buy increasers on margins. Sell decreasers short.The SA during each preceding Time period would be pertinent Wall Street measurement column.

Strategy2:Use a NearestNeighborSetVote strategy. Invest according to the decisiveness of the vote.

Dayn, $AAPL, CLASS, WSM1,1 … WSMw,1 , MSM1,1 … MSMm,1 ,..., WSM1,n … WSMw,n , MSM1,n … MSMm,n

$AAPL record after n days: WSM DayOfWSM DayOfRecordCreation

$AAPL record after n+1 days:

Dayn+1, $AAPL, CLASS, WSM1,1 … WSMw,1 , MSM1,1 … MSMm,1 ,..., WSM1,n … WSMw,n , MSM1,n … MSMm,n , WSM1,n+1 … WSMw,n+1 , MSM1,n+1 … MSMm,n+1

For the distance between these two records, Shift as follows and then SQRT(sum of component differences squared):

Dayn, $AAPL, CLASS, WSM1,1 … WSMw,1 , MSM1,1 … MSMm,1 , . . . , WSM1,n … WSMw,n , MSM1,n … MSMm,n

Dayn+1, $AAPL, CLASS, WSM1,1 … WSMw,1 , MSM1,1 … MSMm,1 , WSM1,2 … WSMw,2 , MSM1,2 … MSMm,2 , . . . , WSM1,n+1 … WSMw,n+1 , MSM1,n+1 … MSMm,n+1

To all 879 and 790 Seminar students:There are many many of you who will be giving a paper (someone elses paper in the seminar and your own paper in the advanced data mining.)Thus we need some mechanism to fit all the papers in the time remaining (and I understand that it will be a few weeks at least before anyone is ready).I propose that you all prepare your presentation as a stand alone presentation with embedded audio (as per my 765 and 789 lecture notes). That way we can all have the benefit of your presentation regardless of the time constraints.

Also, another topic category is to take one of the topic areas of the notes I have been sending (and that are online since 2011 on my web site) prepare a comprehensive set of audio enhanced notes covering what you find in all those notes plus anything you discover while doing that.

Position… 1 2 3 4 5 6 7

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Tweet3 Tweet2

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buy

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1. DpTrees (index by (T,P) )Position 1 2 3 4 5 6 7

Term

AAPL

all

an

and

April

are

always

apple

Test pTrees (e.g., Tweets) 1. DpTrees (indexed by (T,P) )

3. PpTrees (indexed by (T,D) ))

1 0 0 0 0 1 0 . . .

Term a

Etc. PpTrees

Doc Tweet3

Tweet2

Tweet1

OR row gives Term=a ExistentialTerm pTree. Sum gives Term=a DocFreq (df) array

Sentiment analysis (by doc) : PSB: Positive Sentiment BitMap, 1 iff doc has positive AAPL sentiment.PSA: Positive Sentiment Array, measures positive AAPL sentiment level?PSA for each term? Might term context change the sentiment? Position info gives PSV in context! PSB,PSV derived by hand ? (humans r assign PSB,PSV). SA is typically done on small datasets (yesterday’s AAPL tweets) with NLP. We need a Killer Idea? 1.A new way to do SA? (on data Too Big for the Horizontal Guys (TBHG)? 2.A new SA signal which is faster/more_accurate/uses_vertical_data?3.Position info for phrase analysis, but then pTrees are not necessary (id “key” phrases as having same starting point as their first term.).

4D cube for 2-wd phrases?(Overkill?)

Twt3

Tweet2

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0 1 0 0 0 0

2-word phrase start position1 2 3 4 5 6

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0 1 0 0 0 0

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0 0 0 0 0 0

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1 0 0 0 0 1

Aprilappleand

analways.all

againbuy

Tweet1

Doc

s

1st wd

AAPL2nd word

Doc Tweet3 Tweet2 Tweet1

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2. TpTrees (indexed by (D,P) ) Pos 1 2…

Best approach? Compose a historical TBHG (deep and wide) TrainingSet, TS(stock,day,.) containing a column for every pertinent Wall Street and Main Street input (e.g., Russia invades Kiev!) on each stock on each day. Class label: increased/decreased (by a threshold % the next day?). Build a Hulls around the “increased” and “decreased’ classes. Classify each stock each day. Buy increasers on margins. Sell decreasers short.A SA column would be a pertinent Wall Street measurement column.

$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime inSome pretty harsh languagefrom $GLUU - so $AAPL is 0.54% custy of $GLUU . $AAPL is going 64 bit by 15-Feb $GLUU says they aren't going 64bitThe bigger highlighis that $BIDU $BABA Samsung and $AAPL \ could follow\ Xiaomi'slead and make a strategimove with $YOD as wellLooks like $HIVE had to retract their PR yesterda&amp added that they \ DONATED\in support of $AAPL what scumbagshttp://tAnother Sell side analyst making commentson the $HIVE $AAPL news. Says $HIVE isnt worthy of the 50M mkt cap add sees 8M tops hence the fade$CTRL showing up on \ several\sell side reports as a comp. to own into CES &amp for 2015 Home automatia space $AAPL most likely headed into 2015T- Mobile CEO predictifor 2015 - thinks wearables / $AAPL iWatch will be huge http://t$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime inSome pretty harsh languagefrom $GLUU - so $AAPL is 0.54% custy of $GLUU . $AAPL is going 64 bit by 15-Feb $GLUU says they aren't going 64bitThe bigger highlighis that $BIDU $BABA Samsung and $AAPL \ could follow\ Xiaomi'slead and make a strategimove with $YOD as wellLooks like $HIVE had to retract their PR yesterda&amp added that they \ DONATED\in support of $AAPL what scumbagshttp://tAnother Sell side analyst making commentson the $HIVE $AAPL news. Says $HIVE isnt worthy of the 50M mkt cap add sees 8M tops hence the fade$CTRL showing up on \ several\sell side reports as a comp. to own into CES &amp for 2015 Home automatia space $AAPL most likely headed into 2015T- Mobile CEO predictifor 2015 - thinks wearables / $AAPL iWatch will be huge http://t$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime inSome pretty harsh languagefrom $GLUU - so $AAPL is 0.54% custy of $GLUU . $AAPL is going 64 bit by 15-Feb $GLUU says they aren't going 64bitThe bigger highlighis that $BIDU $BABA Samsung and $AAPL \ could follow\ Xiaomi'slead and make a strategimove with $YOD as wellLooks like $HIVE had to retract their PR yesterda&amp added that they \ DONATED\in support of $AAPL what scumbagshttp://tAnother Sell side analyst making commentson the $HIVE $AAPL news. Says $HIVE isnt worthy of the 50M mkt cap add sees 8M tops hence the fade$CTRL showing up on \ several\sell side reports as a comp. to own into CES &amp for 2015 Home automatia space $AAPL most likely headed intoT- Mobile CEO predictifor 2015 - thinks wearables / $AAPL iWatch will be huge http://t$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115 Time : 2014-11-Retweets:6 Favourit:0 User : Marc Lehman Follower:20502}Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime inSome pretty harsh languagefrom $GLUU - so $AAPL is 0.54% custy of $GLUU . $AAPL is going 64 bit by 15-Feb $GLUU says they aren't going 64bitThe bigger highlighis that $BIDU $BABA Samsung and $AAPL \ could follow\ Xiaomi'slead and make a strategimove with $YOD as wellLooks like $HIVE had to retract their PR yesterda&amp added that they \ DONATED\in support of $AAPL what scumbagshttp://tAnother Sell side analyst making commentson the $HIVE $AAPL news. Says $HIVE isnt worthy of the 50M mkt cap add sees 8M tops hence the fade$CTRL showing up on \ several\sell side reports as a comp. to own into CES &amp for 2015 Home automatia space $AAPL most likely headed intoT- Mobile CEO predictifor 2015 - thinks wearables / $AAPL iWatch will be huge http://t$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime inSome pretty harsh languagefrom $GLUU - so $AAPL is 0.54% custy of $GLUU . $AAPL is going 64 bit by 15-Feb $GLUU says they aren't going 64bitThe bigger highlighis that $BIDU $BABA Samsung and $AAPL \ could follow\ Xiaomi'slead and make a strategimove with $YOD as wellLooks like $HIVE had to retract their PR yesterda&amp added that they \ DONATED\in support of $AAPL what scumbagshttp://tAnother Sell side analyst making commentson the $HIVE $AAPL news. Says $HIVE isnt worthy of the 50M mkt cap add sees 8M tops hence the fade$CTRL showing up on \ several\sell side reports as a comp. to own into CES &amp for 2015 Home automatia space $AAPL most likely headed intoT- Mobile CEO predictifor 2015 - thinks wearables / $AAPL iWatch will be huge http://t$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime inSome pretty harsh languagefrom $GLUU - so $AAPL is 0.54% custy of $GLUU . $AAPL is going 64 bit by 15-Feb $GLUU says they aren't going 64bitThe bigger highlighis that $BIDU $BABA Samsung and $AAPL \ could follow\ Xiaomi'slead and make a strategimove with $YOD as wellLooks like $HIVE had to retract their PR yesterda&amp added that they \ DONATED\in support of $AAPL what scumbagshttp://tAnother Sell side analyst making commentson the $HIVE $AAPL news. Says $HIVE isnt worthy of the 50M mkt cap add sees 8M tops hence the fade$CTRL showing up on \ several\sell side reports as a comp. to own into CES &amp for 2015 Home automatia space $AAPL most likely headed intoT- Mobile CEO predictifor 2015 - thinks wearables / $AAPL iWatch will be huge http://t$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime inSome pretty harsh languagefrom $GLUU - so $AAPL is 0.54% custy of $GLUU . $AAPL is going 64 bit by 15-Feb $GLUU says they aren't going 64bitThe bigger highlighis that $BIDU $BABA Samsung and $AAPL \ could follow\ Xiaomi'slead and make a strategimove with $YOD as wellLooks like $HIVE had to retract their PR yesterda&amp added that they \ DONATED\in support of $AAPL what scumbagshttp://tAnother Sell side analyst making commentson the $HIVE $AAPL news. Says $HIVE isnt worthy of the 50M mkt cap add sees 8M tops hence the fade$CTRL showing up on \ several\sell side reports as a comp. to own into CES &amp for 2015 Home automatia space $AAPL most likely headed into 2015T- Mobile CEO predictifor 2015 - thinks wearables / $AAPL iWatch will be huge http://t$IDTI speakingat Barclaysnext week - ConsumerElectronShow (JAN) - Mobile World Congress- $AAPL iWatch chargingtheme fuego for 15Craig Hallum and OTR Global both very high on $IDTI - citing a big potentiawin at LG and $AAPL (iwatch potentiaKaty Huberty (Morgan Stanley)also has a \ bull case\ on $AAPL and raises to 150 with prior tgt of 135 using a 10 EPS tgt. for CY 2015Katy Huberty (Morgan Stanley)taking Apple $AAPL target up to 126 from 115Sell side said $AAPL teardownwont be out till 2015but belief is that $IDTI won the wirelesschargingsocket. Let the chart guru's chime in

175678918012345678919012345678920012345678921012345678922012345678923012345678924012345678925012345256

(Always set the Stride to a multiple of the register size!)A2564 (256 AAPL tweets)

A2564 (256 AAPL Tweets, 296 Terms, 29 Pos)PpTrees (Doc1) 2Level, Stride=6 T# T#

258

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P#12345678910123456789201234567829

115

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

26

00 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

161

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

19

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

31

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

114

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

258

115

26

161

19

31

114

T# Level=1258

1 0 0 000

115

1 0 0 000

26

1 0 0 000

161 1000 0 0

19 0 1 0 000

31 0 1 0000

114

0 10000

T# Level=0258

1 0 0 000

115

01 0 000

26

001 000

161 000100

19 100000

31 001000

114

0 0010 0

Concatenate all levels? AND, analyze, count (if multiple 1-bits in a stride, duplicate the next level down (and indicate where dups are by a mask?).

T# Level=all

TpTrees (Doc1) P# T# 2 3 5 7 9 10 1 0 0 0 0 0 0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 4 0 0 0 0 0 0 5 0 0 0 0 0 0 6 0 0 0 0 0 0 7 0 0 0 0 0 0 8 0 0 0 0 0 0 9 0 0 0 0 0 0 10 0 0 0 0 0 0 11 0 0 0 0 0 0 12 0 0 0 0 0 0 13 0 0 0 0 0 0 14 0 0 0 0 0 0 15 0 0 0 0 0 0 16 0 0 0 0 0 0 17 0 0 0 0 0 0 18 0 0 0 0 0 0 19 0 0 0 1 0 0 20 0 0 0 0 0 0 21 0 0 0 0 0 0 22 0 0 0 0 0 0 23 0 0 0 0 0 0 24 0 0 0 0 0 0 25 0 0 0 0 0 0 26 0 1 0 0 0 0 27 0 0 0 0 0 0 28 0 0 0 0 0 0 29 0 0 0 0 0 0 30 0 0 0 0 0 0 31 0 0 0 0 1 0 32 0 0 0 0 0 0 33 0 0 0 0 0 0 34 0 0 0 0 0 0 35 0 0 0 0 0 0 36 0 0 0 0 0 0 37 0 0 0 0 0 0 38 0 0 0 0 0 0 39 0 0 0 0 0 0 40 0 0 0 0 0 0 41 0 0 0 0 0 0 42 0 0 0 0 0 0 43 0 0 0 0 0 0 44 0 0 0 0 0 0 45 0 0 0 0 0 0 46 0 0 0 0 0 0 47 0 0 0 0 0 0 48 0 0 0 0 0 0 49 0 0 0 0 0 0 50 0 0 0 0 0 0 51 0 0 0 0 0 0 52 0 0 0 0 0 0 53 0 0 0 0 0 0 54 0 0 0 0 0 0 55 0 0 0 0 0 0 56 0 0 0 0 0 0 57 0 0 0 0 0 0 58 0 0 0 0 0 0 59 0 0 0 0 0 0 60 0 0 0 0 0 0 61 0 0 0 0 0 0 62 0 0 0 0 0 0 63 0 0 0 0 0 0 64 0 0 0 0 0 0 65 0 0 0 0 0 0 66 0 0 0 0 0 0 67 0 0 0 0 0 0 68 0 0 0 0 0 0 69 0 0 0 0 0 0 70 0 0 0 0 0 0 71 0 0 0 0 0 0 72 0 0 0 0 0 0 73 0 0 0 0 0 0 74 0 0 0 0 0 0 75 0 0 0 0 0 0 76 0 0 0 0 0 0 77 0 0 0 0 0 0 78 0 0 0 0 0 0 79 0 0 0 0 0 0 80 0 0 0 0 0 0 81 0 0 0 0 0 0 82 0 0 0 0 0 0 83 0 0 0 0 0 0 84 0 0 0 0 0 0 85 0 0 0 0 0 0 86 0 0 0 0 0 0 87 0 0 0 0 0 0 88 0 0 0 0 0 0 89 0 0 0 0 0 0 90 0 0 0 0 0 0 91 0 0 0 0 0 0 92 0 0 0 0 0 0 93 0 0 0 0 0 0 94 0 0 0 0 0 0 95 0 0 0 0 0 0 96 0 0 0 0 0 0 97 0 0 0 0 0 0 98 0 0 0 0 0 0 99 0 0 0 0 0 0

100 0 0 0 0 0 0101 0 0 0 0 0 0102 0 0 0 0 0 0103 0 0 0 0 0 0104 0 0 0 0 0 0105 0 0 0 0 0 0106 0 0 0 0 0 0107 0 0 0 0 0 0108 0 0 0 0 0 0109 0 0 0 0 0 0110 0 0 0 0 0 0111 0 0 0 0 0 0112 0 0 0 0 0 0113 0 0 0 0 0 0114 0 0 0 0 0 1115 1 0 0 0 0 0116 0 0 0 0 0 0117 0 0 0 0 0 0118 0 0 0 0 0 0119 0 0 0 0 0 0120 0 0 0 0 0 0121 0 0 0 0 0 0122 0 0 0 0 0 0123 0 0 0 0 0 0124 0 0 0 0 0 0125 0 0 0 0 0 0126 0 0 0 0 0 0127 0 0 0 0 0 0128 0 0 0 0 0 0129 0 0 0 0 0 0130 0 0 0 0 0 0131 0 0 0 0 0 0132 0 0 0 0 0 0133 0 0 0 0 0 0134 0 0 0 0 0 0135 0 0 0 0 0 0136 0 0 0 0 0 0137 0 0 0 0 0 0138 0 0 0 0 0 0139 0 0 0 0 0 0140 0 0 0 0 0 0141 0 0 0 0 0 0142 0 0 0 0 0 0143 0 0 0 0 0 0144 0 0 0 0 0 0145 0 0 0 0 0 0146 0 0 0 0 0 0147 0 0 0 0 0 0148 0 0 0 0 0 0149 0 0 0 0 0 0150 0 0 0 0 0 0151 0 0 0 0 0 0152 0 0 0 0 0 0153 0 0 0 0 0 0154 0 0 0 0 0 0155 0 0 0 0 0 0156 0 0 0 0 0 0157 0 0 0 0 0 0158 0 0 0 0 0 0159 0 0 0 0 0 0160 0 0 0 0 0 0161 0 0 1 0 0 0162 0 0 0 0 0 0163 0 0 0 0 0 0164 0 0 0 0 0 0165 0 0 0 0 0 0166 0 0 0 0 0 0167 0 0 0 0 0 0168 0 0 0 0 0 0169 0 0 0 0 0 0170 0 0 0 0 0 0171 0 0 0 0 0 0172 0 0 0 0 0 0173 0 0 0 0 0 0174 0 0 0 0 0 0175 0 0 0 0 0 0176 0 0 0 0 0 0177 0 0 0 0 0 0178 0 0 0 0 0 0179 0 0 0 0 0 0180 0 0 0 0 0 0181 0 0 0 0 0 0182 0 0 0 0 0 0183 0 0 0 0 0 0184 0 0 0 0 0 0185 0 0 0 0 0 0186 0 0 0 0 0 0187 0 0 0 0 0 0188 0 0 0 0 0 0189 0 0 0 0 0 0190 0 0 0 0 0 0191 0 0 0 0 0 0192 0 0 0 0 0 0193 0 0 0 0 0 0194 0 0 0 0 0 0195 0 0 0 0 0 0196 0 0 0 0 0 0197 0 0 0 0 0 0198 0 0 0 0 0 0199 0 0 0 0 0 0

200 0 0 0 0 0 0201 0 0 0 0 0 0202 0 0 0 0 0 0203 0 0 0 0 0 0204 0 0 0 0 0 0205 0 0 0 0 0 0206 0 0 0 0 0 0207 0 0 0 0 0 0208 0 0 0 0 0 0209 0 0 0 0 0 0210 0 0 0 0 0 0211 0 0 0 0 0 0212 0 0 0 0 0 0213 0 0 0 0 0 0214 0 0 0 0 0 0215 0 0 0 0 0 0216 0 0 0 0 0 0217 0 0 0 0 0 0218 0 0 0 0 0 0219 0 0 0 0 0 0220 0 0 0 0 0 0221 0 0 0 0 0 0222 0 0 0 0 0 0223 0 0 0 0 0 0224 0 0 0 0 0 0225 0 0 0 0 0 0226 0 0 0 0 0 0227 0 0 0 0 0 0228 0 0 0 0 0 0229 0 0 0 0 0 0230 0 0 0 0 0 0231 0 0 0 0 0 0232 0 0 0 0 0 0233 0 0 0 0 0 0234 0 0 0 0 0 0235 0 0 0 0 0 0236 0 0 0 0 0 0237 0 0 0 0 0 0238 0 0 0 0 0 0239 0 0 0 0 0 0240 0 0 0 0 0 0241 0 0 0 0 0 0242 0 0 0 0 0 0243 0 0 0 0 0 0244 0 0 0 0 0 0245 0 0 0 0 0 0246 0 0 0 0 0 0247 0 0 0 0 0 0248 0 0 0 0 0 0249 0 0 0 0 0 0250 0 0 0 0 0 0251 0 0 0 0 0 0222 0 0 0 0 0 0253 0 0 0 0 0 0254 0 0 0 0 0 0255 0 0 0 0 0 0256 0 0 0 0 0 0257 0 0 0 0 0 0258 0 0 0 0 0 0259 0 0 0 0 0 0260 0 0 0 0 0 0261 0 0 0 0 0 0262 0 0 0 0 0 0263 0 0 0 0 0 0264 0 0 0 0 0 0265 0 0 0 0 0 0266 0 0 0 0 0 0227 0 0 0 0 0 0268 0 0 0 0 0 0269 0 0 0 0 0 0270 0 0 0 0 0 0271 0 0 0 0 0 0272 0 0 0 0 0 0273 0 0 0 0 0 0274 0 0 0 0 0 0275 0 0 0 0 0 0276 0 0 0 0 0 0277 0 0 0 0 0 0278 0 0 0 0 0 02790 0 0 0 0 02800 0 0 0 0 0281 0 0 0 0 0 0 282 0 0 0 0 0 0283 0 0 0 0 0 0284 0 0 0 0 0 0285 0 0 0 0 0 02860 0 0 0 0 0287 0 0 0 0 0 0288 0 0 0 0 0 0289 0 0 0 0 0 0290 0 0 0 0 0 0 291 0 0 0 0 0 0292 0 0 0 0 0 0293 0 0 0 0 0 0294 0 0 0 0 0 0295 0 0 0 0 0 0296 0 0 0 0 0 0

TpTrees 3Level Sri=10 (Doc1)2

01

3

10

0100000000

0000100000

0010000000

0000010000

5

01

0000001000

1000000000

7

10

0100000000

0000000010

9

10

0001000000

1000000000

10

01

0100000000

0001000000

0000000000000000000000010000000000000000000000000000000000000000000000…0

DpTree$ZAGG,P=1D# 12345678910123456789201234567893012345678940123456789501234567896012345678970...256

0000000000100000000000000000000000000000000000000000000000000000000000…0

DpTree $VZ,P=20D# 12345678910123456789201234567893012345678940123456789501234567896012345678970...256

Actual sizes (not T=296, P=29, D=256)

TpTtrees: 1Lev: T=100K=217bits of 248 pTrees.

3Lev: Stri=26=64, 256=28bits 248

pTrees=256 bits (3Lev reduces each pTree by factor of 512. Processing step seldom > ~100s of 248 pTrees)

PpTtrees: 1Lev P=256=28bits 257pTrees =265bits Processing seldom> ~100s of 257 pTrees

DpTtrees: 1Lev D=1T=240b 225 pTrees(=265b5Levs: Stri=28=256 28b 225 pTrees5Lev DpTrees reduce each pTree by factor ~232=4B.

May be >1 1-bit in each (T,P)DpTree.

For most (T,P)’s there are no 1-bits. i.e., the (T,P)pTree is pure0

DpTreeSet is stored in optimal processing form and losslessly as 225=~32,000,000 Levels=5 Strides=64 Concatenated DpTrees.That’s ~235bits = 232bytes = 4B bytes

91 RT 92 rexxam 93 retract 94 retail 95 resgined 96 reports 97 reporting 98 reiterated 99 reit100 REIT101 Redog.great102 Redog103 raising104 raises105 Put106 products107 proceed108 price109 pretty110 presenting111 prepping112 predictions113 PR114 potential).115 potential116 poor117 Pipers118 phones119 Phison120 Perhing121 people122 pay123 Partners124 own125 out126 ouch!!127 OTR128 opening129 NYU130 now131 note132 not133 no134 nite135 nice136 next137 news.138 new139 New140 Need141 Munster142 Multiple143 movie144 Morgans145 Monday146 Mobile147 Mkts148 Minster149 mid150 Markets151 making152 LQMT153 Lotta154 Looks155 looking156 long?157 link158 line159 likely160 like161 LG162 level163 less164 later165 language166 Korea167 key\168 Katy169 JPM170 JP171 journalism172 jefferies173 Jefferies174 JCP175 iWatch176 isnt177 is178 iphone179 iPhone180 initating

271 Apple272 Another273 analyst274 Amazon275 alarm276 ago 277 again 278 admits 279 added280 Activatrions281 activations282 activated283 Ackman 284 Ackman 285 AAPL’s 286 910 287 65 288 64bit 289 4.3M 290 2015 291 126 292 115 293 1111 294 1001 295 1000 296 0.54%

0000000000000100000000000000000000000000000000000000000000000000000000…0

DpTree gap P=8D# 12345678910123456789201234567893012345678940123456789501234567896012345678970...256

DpTrees 2Level, Str=16

0100000000000000

0000000100000000

$ZAGGP=1

1000000000000000

0000000000100000

$VZP=20

1000000000000000

0000000000000100

gapP=8

1000000000000000

0000000000000100

gapP=17

1000000000000000

0000000001000000

GeneP=6

1000000000000000

0000000000100000

GeneP=6

0100000000000000

0000000100000000

conferenceP=6

1000000000000000

0000000000000010

conferenceP=13

258

1 0 0 000100000

115

1 0 0 000010000

26

1 0 0 000001000

161 1000 0 0000100

19 0 1 0 000100000

31 0 1 0000001000

114

0 10000000100

DpTrees 3Level, Str=8$ZAGGP=1

1000

$VZP=20

gapP=8

1000

1000

01000000

00100000

01000000

00100000

00000001

00000100

1 $ZAGG 2 $VZ 3 $VHC 4 $T 5 $SIMO 6 $RIMM 7 $OVTI 8 $NFLX 9 $JCP 10 $IDTI 11 $HLF 12 $HIVE 13 $GLUU 14 $FORD 15 $CTRL 16 $BIDU 17 $BABA 18 $AMZN 19 $AAPL 20 you 21 yesterday 22 year. 23 Xiaomi's 24 wrong! 25 World 26 win 27 week 28 Wedge 29 Wedbush 30 wearable 31 Watch 32 very 33 Verizion 34 usual 35 upgrade 36 up 37 unit 38 TV 39 trial 40 trading 41 traction 42 Topeka 43 top 44 tonite 45 tomorrow 46 told 47 today 48 times 49 Time 50 thinks 51 they 52 there 53 their 54 tgt 55 teardown 56 target..why 57 target 58 taking 59 sure 60 suing 61 stream$AAPL 62 store 63 stock 64 Stellar 65 Stanley) 66 Sr. 67 Square 68 spoke 69 speaking 70 Sources 71 soon 72 site 73 side 74 showing 75 Show 76 shots 77 shop 78 several\ 79 service. 80 Sell 81 sell 82 scurrying 83 Schack 84 Says 85 says 86 saying 87 Samsung 88 sales 89 said 90 sabbatical

181 indicating182 Huberty183 http://t.co/SXzX184 http://t.co/Ny6D185 http://t.co/jZVU186 http://t.co/fuCh187 Holly188 highlighted189 highlight190 high191 harsh192 Hallum193 guys194 great195 good196 going197 Global198 Glad199 Gilford.200 get201 Gene202 gap203 gain204 future205 follow\206 follow207 focusing208 flow209 find210 Expect211 event212 essentially213 engineer214 Employees215 Electronics216 early217 down218 double219 documents220 dividend221 defense222 defending223 defended224 deal225 days226 day227 CY228 custy229 Craig230 controller231 Consumer232 confirming233 confident234 conference235 comp.236 comments237 come238 CNBC239 cloud240 citing241 check242 cheap243 chart244 CEO245 ceo246 case\247 Capital248 Cap249 calls250 call251 buy252 Buy253 bulls254 bull255 bubbles!!256 Bill257 bigger258 big259 believe260 belief261 Barclays262 Bad263 back264 AT&T265 astute266 associates267 asking268 around269 Are270 Apple