Category: Data analytics

Storywrangler, ranking tweet ngrams over time

Posted on by Ray in Data analysis/Big Data, Data analytics, Data availability, Data science, Information economy, Strategic Inflection Points

Read a couple of articles the past few weeks on a project in Vermont that has randomly selected 10% of all tweets (150 Billion) since the beginning of Twitter (2008) and can search and rank this tweet corpus for ngrams (1-, 2-, & 3-word phrases). All of these articles were reporting on a Science Advances article: Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter.

Why we need Storywrangler

The challenge with all social media is that it is transient, here now, (mostly) gone tomorrow. That is once posted, if it’s liked/re-posted/re-tweeted it can exist in echoes of the original on the service for some time, and if not, it dies out very quickly never to be seen (externally ever) again. While each of us could potentially see every tweet we have ever created (when this post is published it should be my 5387th tweet on my twitter account) but most of us cannot see this history for others.

All that makes viewing what goes on on social media impossible which leads to a lot of mis-understanding and makes it difficult to analyze. It would be great if we had a way of looking at social media activity in more detail to understand it better.

I wrote about this before (see my Computational anthropology & archeology post) and if anything, the need for such capabilities has become even more important in today’s society.

If only there was a way to examine the twitter-verse. What’s mainly lacking is a corpus of all tweets that have ever been tweeted. A way to slice, dice, search, and rank this text data would be a godsend to understanding (twitter and maybe social) history, in real time.

Storywrangler, has a randomized version of 10% of all tweets since twitter started. And it provides ngram searching and ranking over a specified time interval. It’s not everything but it’s a start.

Storywrangler currently has over 1 trillion (1- to 3- word) ngrams and they support ngram rankings for over 150 different languages.

Google books ngram viewer

The idea for the Storywrangler project came from Google’s books ngram viewer. Google’s ngram viewer has a corpus of Google books, over a time period (from 1800 to 2019) and allows one to search for ngrams (1- to 5-word phrases) over any time period they support.

Google’s ngram viewer charts ngrams with a vertical axis that is the % of all ngrams in their book corpus. One can see the rise and fall of ngrams, e.g., “atomic power”. The phrase “atomic power” peaked in Google books around 1960 at a height of 0.000260% of all 2 word ngrams. The time period level of granularity is a year.

The nice thing about Google books ngram data is you can download their book ngram data yourself. The data is of the form of tab separated list of rows with ngram text (1 to 5 words), year, how many times it occurred that year, on how many pages, on how many books on each row. Google books ngram data is generally about 2 years old.

Unclear just how much data is in Google’s books ngram database but for instance in the 1 gram English fiction list, they show a sample of two rows (the 3,000,000 and 3,000,001 rows) which are the 1978 and 1979 book counts for the word “circumvallate”.

Storywrangler tweet ngram viewer

The usage tab on the Storywrangler website provides a search engine that one can use to input N-grams that you want to search the corpus for and can visualize how their rank changes over time. For example, one can do a similar search on the “atomic power” ngram only for tweets.

From Storywrangler search one can see that peak tweet use of “Atomic Power” and “ATOMIC POWER” occurred somewhere in July of 2020 (only way to see the month is to hover over that line) and it’s rank reached somewhere around ~10,000 highest used tweet 2 word ngram during that time.

It’s interesting to see that ngram books and ngram twitter don’t seem to have any correlation. For example the prior best ranking for atomic power (~200Kth highest) was in June of 2015. There was no similar peak for book ngrams of the phrase.

For Storywrangler you can download a JSON or CSV version of the charts displayed. It’s not the complete ngram history that Google book ngram viewer provides. Storywrangler data is generally about 2 days old.

The other nice thing about Storywrangler is under the real-time tab it will show you ngram rankings at 15 minute intervals for whatever timeline you wish to see. Also under the trending tab it will show you the changing ranks for the top 5 ngrams over a selected time period. And the languagetab will do tracking for tweet language use for select languages. The common tab will track the ranking of most common ngrams (pretty boring mostly articles/prepositions) over time. And for any of these searches one can turn on or off retweet counting, which can help to eliminate bot activity.

Storywrangler provides a number of other statistics for ngrams other than just ranking such as odds (of occurring) and frequency (of occurrence). And one can also track rank change, old (years) rank vs. current (year) rank, rank (turbulence) divergence.

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Comments?

Photo Credit(s):

Using AI to identify research to invest in

Posted on by Ray in Artificial Intelligence, Data analytics, Deep Learning, Forecasting, Machine Learning, R&D measures, Visionary leadershp

Saw an article from MIT News (Using machine learning to predict high-impact research) on how researchers there were able to train an AI model to predict which scientific research was going to be the most impactful (foundational) over time. The news article was reporting on research written up in a Nature article (Learning on knowledge graph dynamics provides an early warning of impactful research, behind paywall). The researchers proposed that institutions and VC should use their new DELPHI (Dynamic Early-warning by Learning to Predict High Impact [research]) tool to find foundational research to invest in.

Attempts to identify good research have been active for years. For example, CiteSeerX and other’s like them, use an articles citation index to rank research. Citation indexes are sort of like Google’s page rank and uses a count of how many citations a research paper has garnered since publication as their metric of importance.

Although citation indices are a single, easy to calculate metric, they don’t seem to be a foolproof method to identify foundational research and it takes a number of years to become evident. The researchers at MIT decided to see if using an AI model to identify high impact research would work better.

But first please take our new poll:

How DELPHI works

Apparently, DELPHI uses article metadata, such as one can find looking at the Nature article behind this research (linked to above), to create a knowledge graph. They then use the knowledge graph and an AI model to predict whether the research will become high impact or not. The threshold they used for their publication was any research DELPHI predicts would be in the top 5% of all research in a domain.

Not having access to their paper (or code, see below), we can’t determine if they used a DNN or some other AI/data analytics approach to come up with their prediction.

The input data (article metadata) came from a website, Lens.org which provides metadata for ~230M research articles and ~130M patent filings. The researchers focused on life sciences as the domain to analyze to predict impact, but presumably their approach would work on any scientific domain.

The research analyzed all scientific articles for 42 life sciences journals (listed in articles supplementary information). They used as their training set articles written prior to 2017. And then used their model to predict the impact for articles published since 2018.

In the Nature article’s supplementary information they provide a table (Table 2) which lists some of life-sciences articles since 2018 that DELPHI predicts will have high ((top 5%) impact . There’s ~50 articles listed in the table and they supply the (knowledge) Full-graph (citation) count as well as citation counts for the articles.

2nd of 3 pages for table 2 in Nature article’s supplementary information

The Nature article’s home page also list links to the researchers code and data on one of the researchers GitHub repos. When I attempted to download the trained model and sample dataset, it generated a “links had expired” error message from Dropbox . The repo readme file suggested reaching out to the researcher if this happened. We did that, but had not received any response prior to this post’s publication. .

In any case, in the GitHub repository, there are a sample Jupiter notebook and dockerfile used to create a container to run the notebook in. The data they supplied, supposedly is a sample of 206 articles (metadata) and the notebook uses their model to predicts the impact level for those sample articles .

I would have liked to see more information on their model layer structure, hyper-parameters and other model information as well as prediction reliability statistics. But perhaps this is outlined in the Nature article or provided in the model download.

But the approach seems sound enough and even if the researchers didn’t use a DNN, it would easily lend itself to a DNN prediction, assuming you could :

1. Algorithmically create the knowledge graph from article metadata,

2. Digitize and quantify the metadata knowledge graph for all the articles, and

3. Had an independent assessment of impact levels for all research in the training set.

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Now if we could just do this for blog posts and podcasts it might be even more useful (for us).

Comments?

Data Science storage with NetApp’s Python Toolkit

Posted on by Ray in data access, Data analytics, data logistics, Data science, Deep Learning, Machine Learning, Storage

I’ve got a book someplace (yet to be read completely) with the title Data science with Python. At a recent Storage Field Day 21 last month, NetApp was there discussing a number of their product offerings one of which was their Python SDK to manage NetApp storage for data scientists and AI researchers (see videos of their sessions here).

I’m not a data science expert but a Python SDK for storage management just makes so much sense to me I just had to take a look. Their GitHub repo is available online and they call it the NetApp Data Science Toolkit.

But first please take our new poll:

The challenge for data science and AI researchers is that it’s all about the data. How do you find the data, gain access to it, clean it, and process it quickly so you can do it all over again. Having some sort of Python SDK that allows you to do some rudimentary storage volume configuration, access, snapshotting etc. can make these sorts of pipelines be self-serviced rather than going back and forth with operations to get volumes configured, mounted, and services established.

NetApp Data Science Toolkit

The NetApp Data Science Toolkit can be PIP installed into anything with Python 3.5 or later and can be invoked via a command line or as a library of Python functions that can be invoked. The command line utility and the Python calls appear to be functionally equivalent. 

pip3 install netapp-ontap pandas tabulate requests boto3

The Toolkit must be configured for your environment and NetApp storage but once that’s done your ready to rock and roll.

MLOps pipeline from Google

The command line is invoked with 

./ntap_dsutil.py

following that command are subcommands and parameters specifying what ONTAP operation you want to perform and how it is to be done. Python function calls seem to follow the same parameterization as the CLI.

The CLI and Python function calls can run on MacOS or any Linux distribution. There’s a paper that discusses how to use the SDK to accelerate AI pipelines as well as another ReadMe that describes it’s use in Kubernetes with NetApp’s Trident CSI plugin.

The functionality supports NetApp AFF, FAS, Cloud Volumes and Select that are running ONTAP 9.7 or later. For a current list of ONTAP functions available, check out the toolkit. But for a overview these ONTAP functions were available.

  • For Volume Management – cloning, creating, listing all, deleting or mounting a volume,
  • For Snapshot Management – creating, deleting, listing and restoring snapshots (of volumes)
  • For Data Fabric Management – listing all cloud sync relationships, triggering a cloud sync operation, multi-thread pulling a bucket down from S3 storage (into a NetApp volume directory), pulling a single object down from S3 into a file, pushing the contents of a directory to bucket on S3 and pushing a file into an object on S3.
  • For Advanced Data Fabric Management – listing all SnapMirror relationships and triggering a sync operation for an existing SnapMirror relationship.

This is a pretty comprehensive list of NetApp ONTAP storage functionality. Having all this under control of Python and CLI for data scientist or AI researcher seems pretty impressive.

Of course not every option for all those functions are supported but it’s just a start (V1.1 of the toolkit). I’m sure there’s more to come, especially if customers demand it.

However, it would be nice to have an ONTAP simulator available with the toolkit that could be used to test out your Python code and CLI commands before using real NetApp storage. This would be very useful for those of us lacking our own test ONTAP storage, just hanging around on prem or in the cloud.

As Python becomes the language of choice for AI and now data science, it seems only natural that storage and data protection companies would start releasing Python SDKs/APIs for their product functionality. That way AI and data science researchers could embed any storage functionality they needed directly into their Python code or Jupyter Notebook application.

Having a Python SDK for NetApp ONTAP storage, means using data storage for your MLops or data science pipelines is that much easier.

Great move by NetApp. Ok where’s the rest of the industry?

Picture credit(s):

Where should IoT data be processed – part 1

Posted on by Ray in Data analytics, Data compression, Data efficiency, Data growth, Data transmission, Decision making, Deep Learning, Distributed computing, Internet of Things, Mobile computing, Networking, Neural network, Robots, Storage, System effectiveness

I was at FlashMemorySummit 2019 (FMS2019) this week and there was a lot of talk about computational storage (see our GBoS podcast with Scott Shadley, NGD Systems). There was also a lot of discussion about IoT and the need for data processing done at the edge (or in near-edge computing centers/edge clouds).

At the show, I was talking with Tom Leyden of Excelero and he mentioned there was a real need for some insight on how to determine where IoT data should be processed.

For our discussion let’s assume a multi-layered IoT architecture, with 1000s of sensors at the edge, 100s of near-edge processing/multiplexing stations, and 1 to 3 core data center or cloud regions. Data comes in from the sensors, is sent to near-edge processing/multiplexing and then to the core data center/cloud.

Data size

Dans la nuit des images (Grand Palais) by dalbera (cc) (from flickr)
Dans la nuit des images (Grand Palais) by dalbera (cc) (from flickr)

When deciding where to process data one key aspect is the size of the data. Tin GB or TB but given today’s world, can be PB as well. This lone parameter has multiple impacts and can affect many other considerations, such as the cost and time to transfer the data, cost of data storage, amount of time to process the data, etc. All of these sub-factors include the size of the data to be processed.

Data size can be the largest single determinant of where to process the data. If we are talking about GB of data, it could probably be processed anywhere from the sensor edge, to near-edge station, to core. But if we are talking about TB the processing requirements and time go up substantially and are unlikely to be available at the sensor edge, and may not be available at the near-edge station. And PB take this up to a whole other level and may require processing only at the core due to the infrastructure requirements.

Processing criticality

Human or machine safety may depend on quick processing of sensor data, e. g. in a self-driving car or a factory floor, flood guages, etc.. In these cases, some amount of data (sufficient to insure human/machinge safety) needs to be done at the lowest point in the hierarchy, with the processing power to perform this activity.

This could be in the self-driving car or factory automation that controls a mechanism. Similar situations would probably apply for any robots and auto pilots. Anywhere some IoT sensor array was used to control an entity, that could jeopardize the life of human(s) or the safety of machines would need to do safety level processing at the lowest level in the hierarchy.

If processing doesn’t involve safety, then it could potentially be done at the near-edge stations or at the core. .

Processing time and infrastructure requirements

Although we talked about this in data size above, infrastructure requirements must also play a part in where data is processed. Yes sensors are getting more intelligent and the same goes for near-edge stations. But if you’re processing the data multiple times, say for deep learning, it’s probably better to do this where there’s a bunch of GPUs and some way of keeping the data pipeline running efficiently. The same applies to any data analytics that distributes workloads and data across a gaggle of CPU cores, storage devices, network nodes, etc.

There’s also an efficiency component to this. Computational storage is all about how some workloads can better be accomplished at the storage layer. But the concept applies throughout the hierarchy. Given the infrastructure requirements to process the data, there’s probably one place where it makes the most sense to do this. If it takes a 100 CPU cores to process the data in a timely fashion, it’s probably not going to be done at the sensor level.

Data information funnel

We make the assumption that raw data comes in through sensors, and more processed data is sent to higher layers. This would mean at a minimum, some sort of data compression/compaction would need to be done at each layer below the core.

We were at a conference a while back where they talked about updating deep learning neural networks. It’s possible that each near-edge station could perform a mini-deep learning training cycle and share their learning with the core periodicals, which could then send this information back down to the lowest level to be used, (see our Swarm Intelligence @ #HPEDiscover post).

All this means that there’s a minimal level of processing of the data that needs to go on throughout the hierarchy between access point connections.

Pipe availability

binary data flow

The availability of a networking access point may also have some bearing on where data is processed. For example, a self driving car could generate TB of data a day, but access to a high speed, inexpensive data pipe to send that data may be limited to a service bay and/or a garage connection.

So some processing may need to be done between access point connections. This will need to take place at lower levels. That way, there would be no need to send the data while the car is out on the road but rather it could be sent whenever it’s attached to an access point.

Compliance/archive requirements

Any sensor data probably needs to be stored for a long time and as such will need access to a long term archive. Depending on the extent of this data, it may help dictate where processing is done. That is, if all the raw data needs to be held, then maybe the processing of that data can be deferred until it’s already at the core and on it’s way to archive.

However, any safety oriented data processing needs to be done at the lowest level and may need to be reprocessed higher up in the hierachy. This would be done to insure proper safety decisions were made. And needless the say all this data would need to be held.

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I started this post with 40 or more factors but that was overkill. In the above, I tried to summarize the 6 critical factors which I would use to determine where IoT data should be processed.

My intent is in a part 2 to this post to work through some examples. If there’s anyone example that you feel may be instructive, please let me know.

Also, if there’s other factors that you would use to determine where to process IoT data let me know.

Marketing meet Big Data, call records, credit card purchases & demographics

Posted on by Ray in Data analytics, Data annotation, Data discovery, Data science, Market dynamics, Strategic Inflection Points

Read an article in Science Daily (Understanding urban issues through credit cards) that talked about a study published in Nature (Sequences of purchases in credit card data reveals lifestyles of urban populations) that applies big data to B2C marketing.

The researchers examined call data records (CDRs), credit card transactions records (CCRs) and demographic (age, sex, residential zip code, wage level, etc.) data and did a cross table between them to identify sequences of purchases. They then used these sequences to identify different lifestyle groups in the urban area.

Marketing 2.0

The analyzed data from Mexico City, Mexico. The CCR data was collected for 10 weeks across 150K users. The had CDR data for 1/10th of the users for 6 months surrounding the 10 weeks duration. Credit card adoption is still low in Mexico (18%), so the analysis may be biased.  When thy matched CCR expenditures against median wages in a district and they found their participants came from higher wage populations. Their data also spanned all districts within the city.

The analysis identified sequences of purchase categories as well as expenditures.  They characterized purchase sequences as “words”.

 

 

 

Using the word data and further statistical analysis they were able to split the population up into 5 distinct lifestyle groups. 

The loops of icons above represent major purchase categories derived from the CCR data merchant category codes (MCC).  Each of the rings in “a” above show the same 12 major MCC purchase categories. If you look at each ring, one can identify a central or core node that seems to have the most incoming or outgoing arks. These seem to be the central purchases made by that lifestyle group after which they branch out to other purchase categories.

There are five different lifestyle categories (they also show the city average) delineated in the data:

  • Commuter – generally they have to pay tolls, have longer travel between home and work and have a diverse sequence of purchase that occurs after purchases from the toll category.
  • Household – purchases seem to center on grocery stores/supermarkets and then branch off from there.
  • Young – purchases seem to center on the taxicab category and then go to computer-networking, restaurants, grocery stores/supermarkets.
  • Hi-Tech – purchases seem to center on computer-networking,  then go to gas stations, grocery stores/supermarkets, restaurants, and telecomm.
  • Average – seems to have two focuses grocery stores/supermarkets and restaurants and then goes out from there to gas stations, specialty food stores and department stores.
  • Dinner-out – purchases seem to center on restaurants and then branch out fro there to computer-networking, gas stations, supermarkets, fast food, etc.

In “b”  breakout above, you can see the socio-demographic characteristics of each lifestyle group as compared with the median user. And in “c” one can see some population histograms of the demographic data.

They were then able to use the CDR data to construct a map of which lifestyle called which other life style to identify call correlation data. Most calls were contacts between the same groups but the second most active call was calls to householders.

They took this same analysis to another city in Mexico and came up with six  lifestyle categories, five of the same and a different one.

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When I went to Uni (a long long time ago), I attended an urban geography class that was much more scientific and mathematical than any other geography class I had ever attended. I remember asking the professor when did geography become an exact science. As best as I can recall, he laughed and said over the last decade.

Analysis like the above could make B2C marketing, almost an exact science.

Big Data meet Marketing – Buyer beware.

Comments?

Photo Credit(s):  All charts/photos are from the Nature article Sequences of purchase in credit card data reveal lifestyles in urban populations

Stanford Data Lab students hit the ground running…

Posted on by Ray in Data, Data analytics, Data discovery, Data science, Information economy, Visionary leadershp

Read an article (Students confront the messiness of data) today about Stanford’s Data Lab  and how their students are trained to cleanup and analyze real world data.

The Data Lab teaches two courses the Data Challenge Lab course and the Data Impact Lab course. The Challenge Lab is an introductory course in data gathering, cleanup and analysis. The Impact Lab is where advanced students tackle real world, high impact problems through data analysis.

Data Challenge Lab

Their Data Challenge Lab course is a 10 week course with no pre-requisites that teaches students how to analyze real world data to solve problems.

Their are no lectures. You’re given project datasets and the tools to manipulate, visualize and analyze the data. Your goal is to master the tools, cleanup the data and gather insights from the data. Professors are there to provide one on one help so you can step through the data provided and understand how to use the tools.

In the information provided on their website there were no references and no information about the specific tools used in the Data Challenge Lab to manipulate, visualize and analyze the data. From an outsiders’ viewpoint it would be great to have a list of references or even websites describing the tools being used and maybe the datasets that are accessed.

Data Impact Lab

The Data Impact lab course is an independent study course, whose only pre-req is the Data Challenge Lab.

Here students are joined into interdisplinary teams with practitioner partners to tackle ongoing, real world problems with their new data analysis capabilities.

There is no set time frame for the course and it is a non-credit activity. But here students help to solve real world problems.

Current projects in the Impact lab include:

  • The California Poverty Project  to create an interactive map of poverty in California to supply geographic guidance to aid agencies helping the poor
  • The Zambia Malaria Project to create an interactive map of malarial infestation to help NGOs and other agencies target remediation activity.

Previous Impact Lab projects include: the Poverty Alleviation Project to provide a multi-dimensional index of poverty status for areas in Kenya so that NGOs can use these maps to target randomized experiments in poverty eradication and the Data Journalism Project to bring data analysis tools to breaking stories and other journalistic endeavors.

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Courses like these should be much more widely available. It’s almost the analog to the scientific method, only for the 21st century.

Science has gotten to a point, these days, where data analysis is a core discipline that everyone should know how to do. Maybe it doesn’t have to involve Hadoop but rudimentary data analysis, manipulation, and visualization needs to be in everyone’s tool box.

Data 101 anyone?

Photo Credit(s): Big_Data_Prob | KamiPhuc;

Southbound traffic speeds on Masonic avenue on different dates | Eric Fisher;

Unlucky Haiti (1981-2010) | Jer Thorp;

Bristol Cycling Level by Wards 2011 | Sam Saunders

Hitachi and the coming IoT gold rush

Posted on by Ray in Artificial Intelligence, Cloud services, Cloud storage, Data analytics, Distributed computing, Energy efficiency, Information economy, Market dynamics, Object storage, R&D measures, Strategic Inflection Points, System effectiveness, Visionary leadershp

img_7137Earlier this week I attended Hitachi Summit 2016 along with a number of other analysts and Hitachi executives where Hitachi discussed their current and ongoing focus on the IoT (Internet of Things) business.

We have discussed IoT before (see QoM1608: The coming IoT tsunami or not, Extremely low power transistors … new IoT applications). Analysts and companies predict  ~200B IoT devices by 2020 (my QoM prediction is 72.1B 0.7 probability). But in any case there’s a lot of IoT activity going to come online, very shortly. Hitachi is already active in IoT and if anything, wants it to grow, significantly.

Hitachi’s current IoT business

Hitachi is uniquely positioned to take on the IoT business over the coming decades, having a number of current businesses in industrial processes, transportation, energy production, water management, etc. Over time, all these industries and more are becoming much more data driven and smarter as IoT rolls out.

Some metrics indicating the scale of Hitachi’s current IoT business, include:

  • Hitachi is #79 in the Fortune Global 500;
  • Hitachi’s generated $5.4B (FY15) in IoT revenue;
  • Hitachi IoT R&D investment is $2.3B (over 3 years);
  • Hitachi has 15K customers Worldwide and 1400+ partners; and
  • Hitachi spends ~$3B in R&D annually and has 119K patents

img_7142Hitachi has been in the OT (Operational [industrial] Technology) business for over a century now. Hitachi has also had a very successful and ongoing IT business (Hitachi Data Systems) for decades now.  Their main competitors in this IoT business are GE and Siemans but neither have the extensive history in IT that Hitachi has had. But both are working hard to catchup.

Hitachi Rail-as-a-Service

img_7152For one example of what Hitachi is doing in IoT, they have recently won a 27.5 year Rail-as-a-Service contract to upgrade, ticket, maintain and manage all new trains for UK Rail.  This entails upgrading all train rolling stock, provide upgraded rail signaling, traffic management systems, depot and station equipment and ticketing services for all of UK Rail.

img_7153The success and profitability of this Hitachi service offering hinges on their ability to provide more cost efficient rail transport. A key capability they plan to deliver is predictive maintenance.

Today, in UK and most other major rail systems, train high availability is often supplied by using spare rolling stock, that’s pre-positioned and available to call into service, when needed. With Hitachi’s new predictive maintenance capabilities, the plan is to reduce, if not totally eliminate the need for spare rolling stock inventory and keep the new trains running 7X24.

img_7145Hitachi said their new trains capture 48K data items and generate over ~25GB/train/day. All this data, will be fed into their new Hitachi Insight Group Lumada platform which includes Pentaho, HSDP (Hitachi Streaming Data Platform) and their Content Analytics to analyze train data and determine how best to keep the trains running. Behind all this analytical power will no doubt be HDS HCP object store used to keep track of all the train sensor data and other information, Hitachi UCP servers to process it all, and other Hitachi software and hardware to glue it all together.

The new trains and services will be rolled out over time, but there’s a pretty impressive time table. For instance, Hitachi will add 120 new high speed trains to UK Rail by 2018.  About the only thing that Hitachi is not directly responsible for in this Rail-as-a-Service offering, is the communications network for the trains.

Hitachi other IoT offerings

Hitachi is actively seeking other customers for their Rail-as-a-service IoT service offering. But it doesn’t stop there, they would like to offer smart-water-as-a-service, smart-city-as-a-service, digital-energy-as-a-service, etc.

There’s almost nothing that Hitachi currently supplies as industrial products that they wouldn’t consider offering in an X-as-a-service solution. With HDS Lumada Analytics, HCP and HDS storage systems, Hitachi UCP converged infrastructure, Hitachi industrial products, and Hitachi consulting services, together they are primed to take over the IoT-industrial products/services market.

Welcome to the new Hitachi IoT world.

Comments?

Surprises in flash storage IO distributions from 1 month of Nimble Storage customer base

Posted on by Ray in Data analytics, Decision making, Storage performance, System effectiveness

graphs
We were at Nimble Storage (videos of their sessions) for Storage Field Day 10 (SFD10) last week and they presented some interesting IO statistics from data analysis across their 7500 customer install base using InfoSight.

As I understand it, the data are from all customers that have maintenance and are currently connected to InfoSight, their SaaS service solution for Nimble Storage. The data represents all IO over the course of a single month across the customer base. Nimble wrote a white paper summarizing their high level analysis, called Busting the myth of storage block size.
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