thebaldgeek

This one is a little quirky but interesting.

http://plenar.io/

One database. One map.
All data in Plenario exists on a single map and a single timeline, making it incredibly easy to access multiple datasets at once—especially those originally housed at different data portals.
No coding, no NDAs, no navigating government websites older than you are. Accessing the data is as easy as selecting dates and drawing on a map.

What they are saying (selling) is that people want to know about data within a geolocation – so, they are providing a ‘no code’ way of getting it.
Simply draw on their map and it will cough up what ever data you are interested in within that location.

Sounds cool right?
Yeah, cool enough for me to bookmark it, but not use it. Why?
It’s a closed ecosystem. I can’t add my data to their database. (Well, not easily).
I bookmarked it because I don’t think it would be all that hard to re-create in a much less refined, but more personally usable form.

If nothing else it gave me pause to think about how to extract data from a data source (database). Humans are visual, we are space aware. We care about what’s around us or over there, a map is an interesting way to find out what is happening in said location.

This one sort of took me by surprise…. Frankly, I’m a little disappointed in myself that I did not think about it or see it as the real need it is.

http://radar.oreilly.com/2014/12/fast-data-calls-for-new-ways-to-manage-its-flow.html

Fast data calls for new ways to manage its flow.

Examples of multi-layer, three-tier data-processing architecture.

Like CPU caches, which tend to be arranged in multiple levels, modern organizations direct their data into different data stores under the principle that a small amount is needed for real-time decisions and the rest for long-range business decisions. This article looks at options for data storage, focusing on one that’s particularly appropriate for the “fast data” of big data.

In other words, IoT is going to generate big data. In what type of storage is that data written to?
Well, it depends.
If you need to crunch the data before you pass it to an AI engine, you might want to buffer the data in some fast memory so you can tweak it for the AI brain.
If you don’t care about it, you just want to keep it, then it can go straight to slow but cheap storage.
If you want to compress it before it’s stored, then you might write it to some slower, but not deep storage speed, memory so it can be compressed by a formula that allows you to get the information back out of it at some future stage.

It makes sense that you would need to consider this, but it totally blew past me. At this stage my house is not generating that much data and I am not storing every little event. I am still at the data collecting stage, the noise stage, so have not had to deal with this topic.

Some more food for future thought on this one will be required.

I know, I know, I keep banging on this IoT big data thing. It and I are not going away. ‘Sorry’. (I’m not, but it seems like the right thing to say).

Big data really is everywhere and the importance of correctly mining it and deriving intelligence or information from the noise is critical.

Another example;
http://betanews.com/2014/11/13/phil-neville-reveals-how-big-data-is-now-essential-in-professional-soccer/

With high-tech player tracking and analysis systems gaining serious traction in the world of professional sport, data can now be used to instantly analyze player and team performance like never before.

But this hasn’t always been the case, as we found out from former Manchester United player and coach Phil Neville, who helped to shed some light on the role of big data in professional football and how it has changed throughout his career.

He started off by taking us back to Manchester United’s famous 1999 Champions League final win against Bayern Munich. As surprising as it might sound, he said that there was no data used at all back then.

The team just watched a video of one of Bayern’s previous matches, pointed out a few tactical areas that could be exploited and that was it.

Fast-forward fifteen years and things are very different. Today’s top clubs employ people specifically to analyze data collected on their own players and opposition teams and Neville went so far as to say that these days, “data analysts are almost as crucial as the manager”.

Data is collected on everything: Match performance, training performance, blood tests, heart rates, sleep patterns and much more. But, as Neville rightly pointed out, “the key is knowing how to use the data”.

If handled correctly, this information can be used to create individualized training programs, plan recovery and, perhaps most importantly, predict injury and illness.

We better get used to this. Being tracked, analyzed and informed.
Its spilling over into every aspect of our lives, whether you know it or not, regardless of if you consent to it or not. (Like that article said, even the fans are being analyzed).

Its not going to go away, and neither am I. So if you are sick of reading about the Internet of Things, you are going to have to just shake your head and move along….. I find it interesting, and I blog about things I find interesting… Why am I justifying / excusing / apologising to you lot??

Back to the program.
http://techcrunch.com/2014/11/11/the-rise-of-the-sensornet-4-9bn-connected-things-in-2015-says-gartner/

Gartner is predicting a 30 per cent jump in the number of connected objects in use in the wild from this year to next as sensing connected devices proliferate in an Internet of Things (IoT). In a forecast put out today, the analyst predicts there will be 4.9 billion connected things in use in 2015, up from 3.8 billion this year.

The boom in connected sensing devices will gather pace, with the analyst predicting some 25 billion smart devices in circulation come 2020. In other words, hold onto your breath-sensing seats.

For a little comparative context on the figures, annual smartphone shipments topped 1 billion for the first time at the start of this year, based on IDC’s numbers. Connected things can of course scale much faster than smartphones, being far less complex and having a fraction of the per unit cost.

Gartner expects the automotive sector to see the highest growth rate of installed IoT units in 2015, pegged at 96 per cent, outstripping business and consumer sector growth.

Gartner is a well respected source of tech trends. When they speak, many of us stop what we are doing and listen.
They have updated their prediction of the number of things that will be connected. Its more and sooner than they thought.

There are a few interesting takeaways for me.

1. Smart Power meters lead the charge. So that means real time pricing of electricity is on the way. Once the power utility can provide real time pricing information to the consumer, they can start charging them for the real cost of power. Its been too cheap for too long. You want to run your AC at 6pm, you are soon going to pay for it.

2. Smart street and area lighting. That means LED’s. That means more light pollution (LED’s are more broad spectrum light, yuck for telescopes), but cheaper LED lights for home use (since there will be more of them made).

3. Built in intelligence will be standard in many things. AI. Its coming. I have mentioned it in the past, and will have more to say on it as soon as I can come up for air.

4. Cars. I need to look at this. What aspect of cars will be connected? Will it be like Tesla with their cars, keeping track of owners diligence in charging the battery correctly? Or will insurance providers set their rates based on how aggressively you drive (like they are trialling now with optional add on connected black-box car recorders)? Im wondering what and why this made the list.

5. Its going to be a security nightmare.

You can’t have that many connected things without causing some pretty big waves.
Im getting my surfboard! (Hows that for a call to action!).