Sunday, December 21, 2014

Aweekstweets December 14-20 2014: the week Hollywood caved to Pyongyang

“Bare metal servers”: usually means the metal was, at the very least, smelted & poured into ingots before being pressed into server shape

“Wannabe”: Gondwanaland species that the wallaby evolved from.

I like shopping districts more than shopping malls. With districts,you walk outdoors, see the sky, and breathe fresh air.

RT @kexpplaylist #kexp If You Went Away by Daniel Wilson from Boy Who Cried Thunder EP JK--Good moody one from Ypsilanti MI based artist.

“Guaranteed for life”! For the life of what? A fruit fly?

My next original quick-hit will be on Mon Jan 12.

"Industrial Internet of Things Framework Near" ( ) JK--Effort involves ~ 115 members, incl #IBM, AT&T, Cisco, GE, Intel

"Finding Maturity in Your Metadata Strategy" ( ) JK--Essential but mindnumbing element of #bigdata implementations

New #IBM jk LinkedIn Pulse post: "Cognitive Computing: Undecidable Problems & The Limits Of Algorithmic Cognition" ( )

"The Onion’s 10 best re Google" ( ) JK--My fave: "Google Responds To Privacy Concerns w/Unsettlingly Specific Apology"

#IBM cloud announcements: centers ( ), partnership ( ), contracts ( )

" #IBM Research Scientists Investig8 Use o Cognitive Computing-Based Visual Analytics 4 Skin Cancer Image Analysis" ( )

New blogpost: "Kobielus' Quick-Hits in 2014" ( )

Social sentiment? Black swans & concocted controversy p1:  p2:  Fri #IBMDataMag qh

RT @kexpplaylist #kexp i by Kendrick Lamar JK--2014. Great song. A very soulful slice of very musical hiphop.

Why is Washington’s professional hockey team always shouting? Because they’re all Caps.

New JK blog: "Kobielus #bigdata evangelization blogging output in 2014 (not incl LinkedIn qhits, presos, & tweets)" ( )

My #IBM blog: "Patting Down the Pachyderm: My #BigData Prognostications for 2015" will post early in the week prior to New Year's Day.

"Infographic: Data Science 2015 -- What's Hot & What's Not" ( ) JK--Hmm. This aligns beautifully w/my forthcoming blog

"The Future of #BigData is Wearables" ( ) JK--Sources & consumers of data, but not the big honking enchilada of it all

"Why Neural Nets Look Set 2 Thrash Best Human Go Players For 1st Time" ( ) JK--As w/chess, humans best play each other

RT @kexpplaylist #kexp Talking Union by Pete Seeger from If I Had a Hammer: Songs of Hope JK--Seeger goes straight for jugular on this.

RT @kexpplaylist #kexp Will U Love Me Tomorrow by The Shirelles JK--Goffin's "just a moment's pleasure" lyric was daring for 1960 radio song

So, essentially, Kim Jong Eun can censor what Hollywood product gets shoved at us.

Cogn cmptng? Learning thru associational popul8n o sparse experiential matrices w/fresh clues ( ) Thurs #IBMDataMag qh

New #IBM jk blog: "Machine learning molds the material world" ( )

"Yes, A/B Testing Is Still Necessary" ( ) JK--Don't prejudge the test result before you run the test.

"You Need an Algorithm, Not a Data Scientist" ( ) JK--Really? Who are you going to get to craft & tweak your algorithm?

"Cultural Fault Lines Determine How New Words Spread On Twitter, Say Comp Linguists" ( ) JK--Net anchored in real life

"In #BigData, Shepherding Comes First" ( ) JK--Startups desperately court initial customers & VCs to fund hopes&dreams

"SQL, NoSQL? What's the difference these days?" ( ) JK--I can't believe we're still debating the supposed difference

Diet, exercise, & frame of mind do make a big difference. The older I get, the fitter I get. Stress is simply a headwind that streamlines U

How Big Data is Transforming Design into Something Personal 

Find news & stories on how you can re-imagine work in the #WatsonAnalytics Storybook 

"#IBM #dashDB Exits Beta in a flash!" ( ) JK--IBM's cloud data warehouse on #Bluemix. Get it now 

" #Hadoop and #IBM #PureData System for Analytics." Download new whitepaper: 

"Building intelligent APIs to power your mobile economy". #IBM webcast tomorrow at 11am EST. Register: 

"That Was th Year That Was: Major Data Warehsng Events o '14 (& Predictions for '15)" ( ) JK--Good POV from Mike Schiff

"Why Amazon's Ratings Might Mislead: The Story o Herding Effects" ( ) JK--Yeah, like equity analysts' herding U 2 "buy"

"Can #BigData Machines Analyze Stock Market Sentiment?" ( ) JK--Yeah, duh, but can they do it WELL? Ay, that's the rub!

"Sony's new wearable display transforms any glasses into smartglasses" ( ) JK--Wearing ANY specs makes you LOOK smarter

My personal "meh list" includes "bubble tea." I'd prefer to ingest liquified bubble wrap, if that's what it comes to.

Drafted latest #IBM jk #Dataversity blog: "The Evolving #BigData Fabric Of The Travel Experience"

Drafted next #IBM jk #InfoWorld column: "Using Pervasive Analytics To Drive Sustainable Food Chains"

RT @kexpplaylist #kexp Marquee Moon by Television JK--Love this lyric: "I remember how the darkness doubled, how lightning struck itself."

"ABCs of Internet of Things Consortiums" ( ) JK--Holy cow! I count 14 groups developing IoT standards.

"Stanford 'high-rise' chip takes on IoT #bigdata" ( ) JK--Stacks mem+CPU, reducing time/energy to move data btwn them

"Big Data Challenge: Patients Withhold Medical Info" ( ) JK--Hence endangering their own lives thru misguided privacy

"Searching For Truths In Big, Enormous, Massive Data" ( ) JK--Truth emerges as you layer more "grande" synonyms on it

"How Hotels Using #BigData 2 Help Guests Feel @ Home" ( ) JK--Interesting overview of IHG's analytic-fueled personalizn

Most tiresome new industry discussion thread of 2014: "data scientist" gentry do it thisaway & mere yeoman "statisticians" do it thataway

"Cloud Computing , Big Data and Mobility in 2015" ( ) JK--Good analysis. Demands deeper reading.

"Watson wannabes: 4 open source projects for machine intelligence" ( ) JK--DARPA DeepDive, Apache UIMA, OpenCog, OAQA

"How Enterprise Performance Mgmt Is Similar to Football" ( ) JK--OK, but a 16-point extended analogy strains patience

Curious why Toyota named "Tacoma" pickup after PacNW city. If were "Seattle," it'd be a coffee-fueled hybrid. If "Portland," a 1-speed bike

Why do app developers need a data refinery?  via @IBMAnalytics

Save the Date: #IIUG is coming up soon: San Diego April 26-30. 

Have you seen our new and improved #YouTube channel yet? Take a look! #Informix #IoT …

#Data security? #dashDB has that covered for cloud #analytics. Read the details from the expert. 

Get the #bigdata ebook free and find out how cloud fits, then get #dashDB. Yep, it is included in the book!  #dba

Making Windows a little more "BLU" for the Holidays...   #ibmblu

BLU is now available on Windows for speed in data reporting and analytics!   #ibmblu

Reading Spiegelman's classic graphic novel "Maus" in small doses. Its lugubrious thick dark cartoon inkmanship underlines the grim history.

Drafted latest #IBM blog: "Patting Down the Pachyderm: My #BigData Prognostications for 2015"

Cogntv computing? Undecidable problems & limits o algorthmc cognition p1  p2  Wed #IBMDataMag qh

When people say data scientists should B "storytellers," my mind's eye sees Will Rogers sitting whittling statistical models, spinning yarns

Amusing when Facebook calls out my full name in someone's message. Especially when it's family addressing me casually as "James Kobielus"

"20 Ways to Make Those Boring Annual Predictions Not So Boring" ( ) JK--Hmm. Please avoid stupid Xmas carol parodies.

"Crowdsourced Intelligence Tools" ( ) JK--Svc providers can deliver powerful analytics to midmarket, if respect privacy

Hard cider? What's so hard about it? Just pour it down the hatch. Goes down nice & easy. Seamless, intuitive. No instruction manual needed.

One of the delightful threads in the marriage between two people of different countries is how we never stop mocking each other's accents.

1965. “A Charlie Brown Christmas” put “emphasize” into this 7-yr-old’s working vocabulary. “Why do we need a holiday season 2 emphasize it?”

Yule. My brain flashed a dated association: Euell Gibbons. His roughage-munching name was Johnny Carson laughline: 

Business process optimization? Statistics that shape manufacturing p1:  p2:  Tues #IBMDataMag qh

Posture is important to productivity. I keep my posterior firmly planted in my chair all day. Otherwise, I'd be doing a half-assed job.

" #IBM InterConnect 2015" ( ) JK--Feb 22–26 Las Vegas #IBMDataMag Register soon & save: 

"Lifesaving Insight for ICU of Future" ( ) JK--Case study vid: Emory Univ Hospital uses streaming analytics #IBMDataMag

"Dev Healthcare Apps in Cloud" ( ) JK--Case study vid: DataSkill uses #Bluemix 4 flexible healthcare appdev #IBMDataMag

"Mashing Up Analytics for #BigData" ( ) JK--Case study video: infrastructure & analytics in many industries #IBMDataMag

"Cloud-Based Regulatory Compliance" ( ) JK--Case study: OSRAM Licht AG uses cloud to monitor adherence #IBMDataMag

"Game, Set, & Match" ( ) JK--Case study: Wimbledon 2014 applies analytics 2 offer insight behind every shot #IBMDataMag

#IBMDataMag wins Honorable Mention in Folio Eddie Awards: B-to-B - Standalone Digital Magazine Technology/Telecom ( )

"Playing Atari w/ Deep Reinforcement Learning" ( ) JK--Machine-learning algorithm kicks expert human butt on videogame

"OMG approves standard DMN 1.0" ( ) JK--Dissect decisions, relate to input facts, describe detailed decision logic

Germans preserving the ugly Colossus of Prora. Hitler’s fascist vacationland on the Baltic. What’s the point? Tear the eyesore down!

Often my best work comes when my mind is on other things. Distractions are productive when they’re self-inflicted.

The geniuses at Apple Store didn’t comprehend the need for earbud pads to keep the stupid things in your ears. Chalk up one for Radio Shack

Ah, corduroy. Ya know, I'd forgotten how comfortable corduroy can be. Corduroy it is!

Yardbirds "Shapes of Things" ( ) JK--Putting myself into a year-end prognostication frame of mind.

"2014 Highlights: Strategies & Solutions" ( ) JK--Overview of improvements & key content in #IBMDataMag in year gone by

"How Netflix revolutionised television programming with #BigData" ( ) JK--Rich content metadata plays important role

"Hadoop Happenings: Looking to 2015" ( ) JK--Plenty of links to Hadoop-focused predictions.

RT @kexpplaylist #kexp Black Tambourine by Beck from Guero JK--2005. Love the Bo Diddley-ish guitar groove that powers this.

"Top 5 #BigData Trends Of 2014" ( ) JK--SQL+Hadoop, platf maturity, educ options, cloud emerges, multi-analytic engines

"How #BigData, and Critical Thinking, Lead to Business Value" ( ) JK--Cites/quotes me at length: 

"6 Predictions for #BigData Analytics Mkt in '15" ( ) JK--Security, IoT, monetzn, skill gap, image analytx, storytellng

"10 data science predictions for 2015" ( ) JK--Would be more meaningful if had metrics that data scientists could model

"Dataset Tracks World’s Open Govt Data" ( ) JK--Crowdsrcd proj ranks govts on data openness (transparency+accntability)

"Data-Mining Twtr 4 Mental Health Insights" ( ) JK--"Users publicly mentiond their diagnosis....cues linkd 2 disorders"

RT @kexpplaylist #kexp Casimir Pulaski Day by Sufjan Stevens from Illinois JK--2005. Strikingly beautiful+sad story-song. Literary-grade

"IoT Changing How Food Grown" ( ) JK--Analyze field, predict yield, tailor planting plan, farmer real-time iPad access

"Monitor Shows Exactly How Much Power Each Of Yr Gadgets Sucking From Grid" ( ) JK-- Recogs their electronic signatures

"Researchers build pattern-recog model that acts human" ( ) JK--Case-oriented feature selection & similarity matching

Adv visualiztion? Seeing spaces where numbers & words seamlessly join p1  p2  Mon #IBMDataMag qh

Have decided to follow only those who are original, innovative, & disruptive. If they don't say so in their profile, they probably aren't

When somebody flag an article as "great Friday reading," does that imply that its value flatlines over the weekend, resurrects next Friday?

Deciding who to "friend" on social media is a bit like picking petals off a daisy: "I know U" "I know U not" "I know U" "I know U not" ....

Filled an egregious gap in my music collection. I've loved Luna's work for some time. I bought/downloaded their best-of collection.

RT @kexpplaylist #kexp Look Around by tUnE-yArDs from Nikki Nack JK--2014. Very cool & avant. A bit like Joni Mitchell drunk on electronica

Friday, December 19, 2014

Kobielus' Quick-Hits in 2014

January 17:  Big Science? Overreliance on big data can stunt development of scientific rigor: 
January 21:  Security of big data? Shoddy lifecycle management is ironic data security:  http:  //
January 22:  NoSQL? The architecture that's still curiously absent:  http:  //
January 23:  Context accumulation? Narratives drive home relevance of statistical models :  http:  //
January 24:  Meaty metadata? Data variety leads to metadata viscosity:  http:  //
January 27:  Smarter planet? Intelligence for a self-healing landscape:  http:  //
January 28:  Data monetization? Pay the persons for their personal data:  http:  //
January 29:  Internet of Things? Instrument the birdies, bees, and other beasties:  http:  //
January 30:  Recommendation engines? The untapped potential of video, image, and gesture analytics in retail showrooms:  http:  //
January 31:  Sexy statistics? The vintage kick of old data poured into fresh analytic bottles:  http:  //
February 3:  Peta-governance? Bottom-line ROI from boosting the quality of experience data:  http:  //
February 4:  Smarter planet? Continuous crowdsourcing of quality-of-life data will power livable urban existence:  http:  //
February 6:  Hadoop uber-alles? Climbing the slope of enlightenment, arriving at plateau of productivity:  http:  //
February 7:  Machine learning? Maximum impact from bigger data & deeper learning:  http:  //
February 10:  Ambient analytics? Mobile data traces the contours of urban experience:  http:  //
February 11Data-scientist skillsets? Psychological insights key to modeling customer causationhttp:  //
February 12:  Meaty metadata? Extracting corpus omniscience from big data:  http:  //
February 13:  Complex event processing? Plucking event graphs from the deep, dark, dynamic Web:  http:  //
February 14:  Big data vision? Modern economy mills new value from its own digital exhaust :  http:  //
February 17:  Real-world experiments? Disrupting your enterprise while mitigating the risks of doing so:  http:  //
February 18:  Engaging customer as individual? Abandonment metrics as warnings and/or opportunities:  http:  //
February 19:  Graph analysis? Identifying the happy medium between the under- and overconnected influencers: 
February 20:  Cognitive computing? When biases cloud automated cognition:  http:  //
February 24:  Geospatial analytics? Analytic surveillance in the cause of resource stewardship:  http:  //
February 25:  Healthcare analytics? Tuning the fusion of human physiology & machine physics:  http:  //
February 26:  Gamified analytics? Brownie points for consumers who share their brand love:  http:  //
February 27:  Sexy statistics? Distinguishing hidden (but real) patterns from those that are real-seeming (but bogus):  http:  //
February 28:  Workload-optimized systems? HPC now mostly big data analytics with growing emphasis on small lots & asynchronous processing:  http:  //
March 4:  Storage optimization? Compress what you can, extract insights prior to purging the rest:  http:  //
March 5:  Data-scientist skillsets? Juggling visualizations, algorithms, and narratives:  http:  //
March 6:  Internet of Things? New measurement tools for candid ethnography:  http:  //

March 7:  Advanced visualization? Eyeballing the dark dimensions :  http:  //
March 10:  Healthcare analytics? Sensing, mapping, and mining the mystery of the brain:  http:  //
March 12:  Experience optimization? Big data framing the engagement with art and culture:  http:  //
March 13:  Business process optimization? Plugging a lean, mean analyzing machine into your manufacturing operations:  http:  //
March 17:  Recommendation engines? Analytics grooving with whatever grooves groove you:  http:  //
March 19:  Privacy and big data? Addressing the tricky contours of in-store privacy:  http:  //
March 25:  Privacy and big data? The dangers of misplaced faith in tactical and technological quick-fixes:  http:  //
March 26:  Graph analysis? Apache Spark begins to spark convergence of Hadoop, streaming, in-memory, & graph analysis: 
March 31:  Healthcare analytics? Using advanced image analytics to spot hidden cancer patterns:  https:  //
April 1:  Data journalism?:  http:  //
April 2:  Peta-governance? Where trustworthiness is concerned, the proof is in the data-governance process:  http:  //
April 3:  Prediction markets? Fostering open marketplaces for models and modelers:  http:  //
April 4:  Moneyball?:  http:  //
April 7:  Hadoop uber-alles? Hadoop beginning to stare newer big-data approaches in the face:  http:  //
April 8:  Open data? Climate data should move as freely as the atmosphere that cloaks our warming planet:  http:  //
April 9:  Machine learning? When data scientists struggle to keep their foothold in ground truth :  http:  //
April 10:  Big Science? The rigid regimen of reproducible computational findings:  http:  //
April 11:  Big identity? The big data challenges of identity management in the Internet of Things :  http:  //
April 14:  Machine learning? Automating log-data analysis through unsupervised and reinforcement learning algorithms:  http:  //
April 15:  Internet of Things? The binocular vision and opposable thumb of cognitive computing :  http:  //
April 16:  Context accumulation? Grounding cognitive confidence in the probabilistic fabric of the real world :  http:  //
May 19:  Real-world experiments? The tricky business of A/B testing:  http:  //
May 20:  Storage optimization? Software-defined storage driving the demise of rip-and-replace:  http:  //
May 21:  Experience optimization? Drilling into the messy gusher of web analytics data:  http:  //
May 22:  Machine learning? A melting pot for today's leading-edge advanced analytics:  http:  //
May 23:  Sexy statistics? Big-data's correlations and cautionary tales:  http:  //
May 27:  Big-data discovery? The power of Bayesian search:  http:  //
May 28:  Open data? The democratization of standardized data in civic governance:  http:  //
May 29:  Big Data's optimal deployment model? Deeply embedded in the cloud:  http:  //
May 30:  Machine learning? Deep learning to filter text for the known, unknown, and unknowable unknowns:  http:  //
June 2:  Engaging customer as individual? Cognitive computing, conversational engagement, & customer confidence:  http:  //
June 3:  Internet of Things? Digitally fingerprinting the trusted endpoint:  http:  //
June 4:  Big identity? Using big data analytics to identify & shut down slippery cyberscammers:  http:  //
June 5:  Experience optimization? Internet of Things, next best actions, & the downside of the technological cocoon:  http:  //
June 6:  Healthcare analytics? Remaining skeptical about the data science behind dietary research:  http:  //
June 9:  Open data? The promise and privacy implications of open access to energy data:  http:  //
June 10:  Big data's optimal deployment model? The niche role for graph databases in hybrid architectures:  http:  //
June 11:  Quantified self?:  http:  //
June 12:  Information economics? The shifting economic role of official government statistics in the era of social listening:  http:  //
June 13:  Hadoop uber-alles? Implementing an extensible library of statistical algorithms & models to serve big-data developers:  http:  //
June 16:  Storage optimization? Data deduplication improves cuts IT costs, boosts data scientist productivity, & bolsters data quality:  http:  //
June 17:  Engaging customer as individual? Mapping the customer journey through the seemingly irrational :  http:  //
June 18:  Data-scientist skillsets? The delicate art of project prioritization and triage:  http:  //
June 19:  Healthcare analytics? Health data brokers and the arms race in intrusive target marketing:  http:  //
June 20:  Big identity? Nonintrusive strong authentication through never-ending behavioral fingerprinting:  http:  //
June 23:  Big Media? The narrative power of video content analytics:  http:  //
June 24:  Quantified self? The social physics of a quantified society:  http:  //
June 25:  Open data? The front line of grass-roots consumer protection:  http:  //
June 26:  Recommendation engines? Fancy math to illuminate stabs in the dark:  http:  //
June 27:  Data journalism? Using real-time analytics to identify who scooped whom online:  http:  //
June 30:  Internet of things? The potential for sensor-driven hyperlocalized weather forecasting:  http:  //
July 1:  Healthcare analytics? Big data as a factor in life-or-death decisions:  http:  //

July 2:  Ambient analytics? The advent of big-data wearables and "unaware-ables":  http:  //
July 3:  Engaging customer as individual? Parrying the double-edge of customer sarcasm:  http:  //
July 7:  Moneyball? Pitcrew analytics and within-race data-driven decision support:  http:  //
July 8:  Big Media? The shifting art of audience measurement in the era of all-online media:  http:  //
July 9:  Big-data development? The agile imperative and the risk of data scientists "boiling the lake":  http:  //
July 10:  All in memory? Scaling in-memory infrastructures up and out :  http:  //
July 11:  Healthcare analytics? Keeping patients from straying off the path to recovery:  http:  //
July 14:  Real-world experiments? Dissecting the Facebook controversy over mood manipulation:  http:  //
July 15:  Security of big data? The imperative and issues surrounding whole-population security analytics:  http:  //
July 16:  Data-scientist skillsets? Data science in the new product development repertoire:  http:  //
July 17:  Open data? Open correlations in the common cause:  http:  //
July 18:  Analytic acceleration in the cloud? The new era of big data as a service:  http:  //
July 21:  Recommendation engines? Predicting the exquisitely nonlinear shifts of customer taste:  http:  //
July 22:  Big-data ethics?:  http:  //
July 23:  All in memory? Transitional patterns on the road to the "all-and-only-in-memory cloud":  http:  //
July 24:  Security of big data? The self-protecting big-data honeypot:  http:  //
July 25:  Advanced analytics?:  http:  //
July 28:  Internet of Things? The walls have ears, eyes, noses, and every other sense organ:  http:  //
July 29:  Data-scientist skillsets? A polymathic grasp of myriad disciplines and applications:  http:  //
July 30:  Big data's optimal deployment model? The core principles of scalability:  http:  //
July 31:  Peta-governance? The challenges of probabilistic data-matching in the Internet of Things:  http:  //
August 1:  Open data? Monetizing your existence as a crowdsourced data scientist:  http:  //
August 4:  Big data on the move? The emergence of the mobile back-end as a service:  http:  //
August 5:  Geospatial analytics? Ammunition against pestilence:  http:  //
August 6:  Decision automation? Retraining and restraining the long-data arm of the law:  http:  //
August 7:  Advanced analytics? Pick an algorithm, any algorithm:  http:  //
August 8:  Conversation optimization? The delicate dance of accessorizing your lifestyle online:  http:  //
August 11:  Cognitive computing? Wrestling the myriad definitions down to manageable size:  http:  //
August 12:  Big Science? The open-sourcing of scientific inquiry throughout the world:  http:  //
August 13:  Data-scientist skillsets? Articulating the advantages of analytics over intuition:  http:  //
August 14:  Healthcare analytics? Wearable cognition-assist analytics as the new prosthetics:  http:  //
August 15:  Big Science? The staggering resource requirements of computational megascience:  http:  //
August 18:  Service-oriented analytics? Big-data analytics consulting as a service:  http:  //
August 19:  Big-data hardcore use cases? Assessing when bigger data truly is better:  http:  //
August 20:  Data-scientist skillsets? Girding yourself for the commoditization of your profession:  http:  //
August 21:  Advanced analytics? The converged and accelerated machine learning of ensemble methods:  http:  //
August 22Data-scientist skillsets? Teaming within the open expertise communitieshttp:  //
August 25:  Data-scientist skillsets? Introducing evidence-driven computational approaches into the public-policy arena:  http:  //
August 26:  Meaty metadata? The analytic potency of the ontology:  http:  //
August 27:  Hadoop uber-alles? Dredging the "data lake" metaphor down to its muddy bottom:  http:  //
August 28:  Machine learning? Distinguishing deep learning from its opposite:  http:  //
August 29:  Big-data-driven TV experience? Serving both active and passive audiences equally:  http:  //
September 2:  Machine learning? Delving into the depths of deep learning:  http:  //
September 3:  Advanced visualization? The analytical value of data-provenance tracking within big-data visualizations:  http:  //
September 4:  Healthcare analytics? Unstructured analytics powering pandemic early-warning systems:  http:  //
September 5:  Data journalism? Deep learning threatens to deep-six journalism's faith in the factuality of the photograph:  http:  //
September 8:  Decision scientists? Data scientists challenged to sway the hearts and minds of public policymakers:  http:  //
September 9:  Big Science? The insight-acceleration potential of elastic storage clouds:  http:  //
September 10:  Sexy statistics? The tricky serendipity of data-lake fishing expeditions:  http:  //
September 11:  Big data's optimal deployment model? "Fog" clouds optimized for Internet of Things analytics:  http:  //
September 12:  Big-data single version of the truth? The practical limits of clue-googling:  http:  //
September 15:  Big Science? The analytical challenges that frustrate use of data science in global studies:  http:  //
September 16:  Internet of Things? Revisiting Metcalfe's Law in the era of everything networking:  http:  //
September 17:  Recommendation engines? Black art of benchmarking against the past and pending:  http:  //
September 18:  Advanced analytics? Please avoid interpreting "advanced" as "hipper than thou":  http:  //

September 19:  Healthcare analytics? Mining hospital data for nonobvious infection and contagion patterns within their facilities:  http:  //
September 22:  Peta-governance? Timing means everything for establishing accountability:  http:  //
September 23:  Advanced analytics? Monte Carlo simulation when the past is an uncertain prologue to prediction:  http:  //
September 24:  Cognitive computing? Acing the Turing test is the least of it :  http:  //
September 25:  Data-scientist skillsets? Immersion in probabilistic programming languages:  http:  //
September 26:  Open data? Equitably distributing data-science brainpower among the haves and have-nots:  http:  //
September 29:  Stream computing? Converging in-motion, in-memory, and in-process analytics:  http:  //
September 30:  Engaging customer as individual? The blurry boundary between engagement, influence, and manipulation:  http:  //
October 1:  Healthcare analytics? Big data's "4 Vs" drive advances in computational bioinformatics:  http:  //
October 2:  Open data? The emergence of the urban data scientist:  http:  //
October 3:  Big Media? Video and image analytics for extracting real-time actionable insights:  http:  //
October 6:  Quantified self? Healthy self-monitoring vs. narcissistic self-obsession:  http:  //
October 7:  Machine data analytics? Man & machine data becoming indistinguishable:  http:  //
October 8:  Hadoop uber-alles? The challenge of staying current on an ever-shifting technology landscape:  http:  //
October 9:  Talent analytics?:  http:  //
October 10:  Big-data discovery? Shining analytical light deeply into dark data:  http:  //
October 13:  Smarter cities?:  http:  //
October 14:  Hadoop uber-alles? Data modeling will endure and you'll still need to pay the ETL piper somewhere sometime:  http:  //
October 15:  Big identity? Facial recognition, deep learning, and the end of anonymity in public spaces:  http:  //
October 16:  Big-data ethics? The difference between targeted segmentation and discriminatory profiling:  http:  //
October 17:  Engaging customer as individual? Quantification of student performance in the new education industry order:  http:  //
October 20:  Workload-optimized systems? Pushing MapReduce's efficiency envelope:  http:  //
October 21:  Machine learning? Need a decision tree for data scientists to choose among machine-learning statistical frameworks:  http:  //
October 22:  Business process optimization? The limits of disintermediation in the cognitive era:  http:  //
October 23:  Talent analytics? Non-obvious patterns of who knows what, does what, and gets what done:  http:  //
October 24:  Machine learning? An evolving grab-bag of magic tricks that still lacks a unifying framework:  http:  //
October 27:  Big data on the move? The evolving data fabric of the travel experience:  http:  //
October 28:  Marketing campaign optimization? Continuous campaigning for mass-market blockbusters:  http:  //
October 29:  Data-scientist skillsets? Getting up to speed on machine learning:  http:  //
October 30:  Quantified self? The delicious demon of self-awareness:  http:  //
October 31:  Chief Data Officer?:  http:  //
November 3:  Data monetization? Nabbing the counterfeiters behind fake online reviews:  http:  //
November 4:  Geospatial analytics? Predictive risk mitigation and retrofitting for disaster preparedness:  http:  //

November 5:  Cognitive computing? Programming the artificial mind:  http:  //
November 6:  Internet of Things? Behavioral analytics in the era of wearables:  http:  //
November 7:  Peta-governance? The potential of graph analytics in master data management:  http:  //
November 10:  Big-data single version of the truth? Curation vs. stewardship in the era of multistructured data:  http:  //
November 11:  Prescriptive analytics? Massive-scale prediction and real-time interdiction in the fight against cybercrime:  http:  //
November 12:  Big Media? Standards-based object-storage platforms are key to streaming media:  http:  //
November 13:  Transactional analytics? Channels cull continuous customer expertise from cognitive cloud:  http:  //
November 14:  Analytic acceleration in the cloud? The next evolution in self-service business analytics:  http:  //
November 17:  Modeling automation? Machine learning shapes the material world:  http:  //
November 18:  Workload-optimized systems? The challenges of scaling to Facebookian proportions:  http:  //
November 19:  Social sentiment as valuable market intelligence? The utility or futility of weeding out bogus online reviews:  http:  //
November 20:  Influence analytics?:  http:  //
November 21:  Big Media? Sentiment data may suffer as social networks evolve into broadcasting media platforms:  http:  //
December 1:  Cognitive computing? Fathoming photos at algorithmic speed:  http:  //
December 2:  Healthcare analytics? The possibility of appliance-enabled whole-body self-diagnosis:  http:  //
December 3:  Healthcare analytics? The electrified “third rail” of deep psychographic customer engagement:  http:  //

December 4:  Experience optimization? Assessing big data’s role in the grand scheme of human happiness:  http:  //

December 5:  Internet of Things? IoT insights that can best be revealed through graph analysis:  http:  //

December 8:  Crowdsourcing Big Data creativity? Intersection of interest graphs with the Internet of Things:  http:  //
December 9:  Geospatial analytics? Managing the land more effectively to protect the rainforest:  http:  //
December 10Smarter planet? Remote sensing the globe from every possible viewpointhttp:  //
December 11:  Marketing campaign optimization? Using pervasive analytics to drive a sustainable food chain:  http:  //
December 12:  Smarter cities? The infrastructure silo-busting imperative:  http:  //
December 15:  Advanced visualization? Seeing the spaces where numbers and words seamlessly join:  http:  //
December 16:  Business process optimization? The statistics that shape manufacturing:  http:  //
December 17:  Cognitive computing? Undecidable problems and the limits of algorithmic cognition:  http:  //
December 18:  Cognitive computing? Learning through associational population of sparse experiential matrices with fresh clues:  http:  //

December 19:  Social sentiment as valuable market intelligence? Black swans and the predictive challenge surrounding concocted controversy:  http:  //