Takenori, those are great ideas! Sent from my iPhone
> On Oct 15, 2015, at 10:58 AM, Matthew Taylor <[email protected]> wrote: > > Hi Takenori, > > I like both your ideas! The first idea may not be an application, but it > seems like it would provide a platform for others creating application, and > it has a potential business use-case. The 2nd idea is very interesting, but > I'm no sure that you will be able to collect enough data. You'll need a lot > of people with smartphones sending data, an app that collects the data you > need, and probably weeks worth of data to have enough for HTM to start > recognizing patterns. > > But I suggest you submit your first idea, it has a lot of potential. Then > maybe while you are building it, you will come up with another app idea? > > Thanks, > > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > >> On Thu, Oct 15, 2015 at 3:05 AM, Takenori Sato <[email protected]> wrote: >> Hello, >> >> I would like to join the HTM Challenge, >> but am not sure at all if mine can meet the requirements. >> >> In the first place, let me explain about my idea. >> >> 1. Amazon S3 extension API for anomaly detection >> >> * PUT bucket anomaly detection >> (parameters) >> - target bucket to store results(anomaly scores/predictions) >> - input information >> - model information >> - output information >> >> We develop and sell S3 compatible object storage software and appliance. In >> Japan, major service providers use our product to offer cloud storage >> service to their end users. >> >> So, imagine an end user uploads a sensor data periodically to the bucket on >> a cloud. In the cloud, a job to produce anomaly scores/predictions get >> scheduled, executed with HTM. And the result becomes available on the target >> bucket. An end user keeps polling the latest anomaly scores on the target >> bucket to see if there's an anomaly, and takes an action as required. Of >> course, it is possible to get historical data as well. >> >> This is a generic APIs for anomaly detection. >> >> 2. Low/Super Low frequency wave detector >> >> Low/Super Low frequency wave is becoming more problematic to human >> health(especially to sleep). >> >> It is not easy to measure, nor impossible to hear. Having difficulty in >> sleep on one night could be because of low frequency wave coming from a >> nearby location. >> >> So I guess it is not a bad idea to measure and record low/super low >> frequency waves with a smart phone, and keep uploading to a cloud. >> >> On the other hand, more and more disasters(earthquakes, landslides, >> eruptions) happen today in Japan. According to some researchers, they are >> observed with low frequency waves. If such an app is used by many people, it >> would detect a disaster before it happens. >> >> In summary, such an low/super low frequency wave data(geospatial temporal >> data) is put on a bucket with anomaly detection enabled, being analyzed by >> HTM as a whole. Then anomaly scores to indicate (unknown)disasters are >> generated, and notified as needed. >> >> >> 1 fully utilizes HTM, but not an application. 2 is an application, but does >> not directly use HTM. >> >> Besides, perhaps it's impossible to get samples of a disaster for a demo in >> one month. >> >> Is this qualified for the challenge? If yes, which part? >> >> Thanks, >> Takenori >
