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Let’s Make Zense

Zense is not a word in any dictionary.  Yet, how could we make sense of something that is not even defined.  Why we care anyway?  If you Google the word, let’s say it is a word, surprisingly you would find the word is used, in one case, to associate the outcome of your feel after the tasting of selective food, or it is the name of a Cafe in Hong Kong, that Cafe Zense does produce a delicious menu of desserts and curated coffees.  Let’s hear our side of the story.

IoT is more noisier than ever.  We remember the upticks of RFID in early 2000, and we did jump in to enable that left jacket pane of a high-end suit to be an IoT in the garment supply chain.  The visibility of supply chain items, from the Italy fabrics, the final sewing of all pieces of a high-end suit jacket, to the final replenishment of that particular jacket to the designated retail shop that puts in the order to the warehouse just an hour ago. The passive RFID tag on each jacket pane brings item level identification, making it a unique IoT, or a visibility item, if detected (by an RFID reader).

In general, an IoT, either by itself or attached to a physical object, carries a sensor, that senses and digitalizes some ambient element of the environment, somehow transmits the data with its built-in communication capability.

We go about our daily routines, be it along the route we always take going to work, or that flight leaving daily from an airport to its destination, or the freshly picked cherries on route to a market that is 8,000 miles away.

The sensor has the ability to sense. For example, in a sensors network, a sensor called, e.g., a temperature module such as that offered for arduino1, can detect a heat index of the surrounding environment, and if the Fahrenheit metric is used, the sensor can be designed to signal a value in degrees of Fahrenheit. The known degree value could mean differently.  Just think about monitoring the storage temperature of covid-19 jab from Pfizer in the supply chain. For example, ice cream, according to one Web information resource, is best to store at  0 (zero) degrees Fahrenheit or lower, but at serving, it is ok to be around 5-10 degrees. So the temperature sensed should be converted to a some text, rather than just a numeric value, for the human to understand the implication, if the temperature is ideal at this point, etc.  Such conversion of a numeric value to a textual information will be processed off sensor site likely.  The off-site processing basically is trying to offer a peace of mind to the end-user who is receiving such sensor data.  The calmness, or such state we called ‘zense,’ can be gained with IoT technology adoption.

From IoT to zense is challenging in areas of communications technology, computing at edge or cloud or even fog as some may claim, and the proper installation of sensors and the networks. This IoT topic is likely be a few main focus in this blog.

Zenser vs Citizen Data Scientist vs You

Data in a computer meant something to me, well, at least as 1’s 0’s, of, say, in ASCII coding. Data is just a string of one’s and zero’s in the binary world. Data, in the digital world, is coded asset that somehow carries meanings to a privileged few, by design, such as that string is your driver’s license number for a DMV, or the arrival time of your flight to your next destination on the flight display board in the departure hall of an airport. There is Big Data. There is IoT Data. And we might agree, data carries information, if properly processed. Given that, what is a science of data? Or is “data science” the same? I am not really ready to discuss this but you will help right?

The word Science, by online Merriam-Webster, is, in one definition, ‘the state of knowing.’ Let me take a leap of faith to say Data Science is a practice, grounded whenever possible by laws and theories, to know about data. And if I randomly look up the definition of a data scientist (DS), and for no specific reason, include a definition here (from this site as “a person who has the knowledge and skills to conduct sophisticated and systematic analyses of data.” Ok, let’s just go with that. Then, the DS is to perform some guided analyses on available data to bring a know in some context to try to meet the need of the acquirer. The gained knowledge might help the acquirer to make some adjustment or decision.

Let’s agree that a DS knows the tools to bring the know out from data, and the context of the know is for the zense of an acquirer, or consumer of the know from the data. Or, a DS knows how to make zense for a consumer. The DS, now in the world of Big Data, needs to keep a comprehensive knowledge of what data is available and how. This knowledge continues to grow, especially with the diverse amount of IoT data, that in some sense, provide a time-sensitive element into the analysis. Zense as defined in is the state of calmness. The calm could be the knowing that the never-ending trade war will bring a 20% reduction in business revenue, instead of the uncertainty of what the magnitude would be. The calm could simply be that your Uber is not likely to be delayed in arrival with the sudden tornado warning in your vicinity. Zense is not measured in negative or in positive energy, just a sense of knowing that brings down the level of emotion from a natural reaction of a consumer.

In sum, we prefer a Zenser label than a DS label, that DS carries out a purposeful analysis every time. Given the numerous data sources, different ‘computing’ is needed. The term ‘ambient computing’ has been used, and we will just leave it as that. Computing is computing just like data is the same as they are one’s and zero’s. Edge-, Cloud- and Fog-computing are the same, just one must consider if there is an advantage to do the computing far from where you stand, or at where you stand, or in-between?

Anyone, that is You, cannot be a data scientist, and any data scientist must develop that purposefulness to be a zenser. We need Zensers in our digital world.

Bookshelf – Then & Now

An article on data management appeared in IEEE Computer recently (Data Management, IEEE Computers, 2017). The author, Lomet, D.B, talks about the cost of storage, and correspondingly the capacity now available at our fingertips, and posts the question of what the ‘world of data will look like in 50 years?”

Maybe 30 years ago, the boxes of IEEE Transactions on Computers hard copy sat in a storage shed. If I had my choice, I would probably display my collection in bookshelves at home or in an office. Yes, you have to go to the University library to get to read one of the issues, and yes, there was not the website IEEE Xplore at that time. The ease of data ‘acquisition’ coupled with data push – those notifications that so many of us are tickled to get, with our mobile devices gives reason that we want terabyte of storage. How often would we go back and look at that Whatsapp message 3 years ago? (Is there a way to do that – to give a date and Whatsapp will provide you the messages of that date? in app? at some website?). Since we do not have books and magazines (how many printed magazines still survive in the digital world?) to adorn our bookcases in our study, could we have some data management tool that is designed like a book that we can shelf, but controlled by our mobile device (best yet, selectable by voice and activated by voice via Siri or Alexa), that my Whatsapp messages of that date will be played out for my guests to enjoy, laughing at what a silly conversational topic then, and how one of the messengers was on target with a silly response. Would that be nice? This could extend to each vacation trip that you took, and a playback is auto by that ‘data book’ on your shelf.

Let’s hope.

Visibility Cloud (original post: November 17, 2011)

Trying here to formulate a better conceptualization of SCV (supply chain visibility) in the cloud. Let me just call this concept: Visibility Clouds. Yes, Clouds as in many clouds – crossing country borders and jurisdictions. We will not touch on those legal and political issues here now. Visibility Clouds is a concept where your self-service visibility is readily available in the cloud across all related supply chains in the clouds. The visibility is on-target when the RFID-IS resides in the clouds as it would be avail for Hadoop-type apps to glean information off RFID event data. What is required for this Visibility Clouds (VCs) is to push with some redesign of the visibility platform (VP) to be active in the cloud. The plug-sync-play by each supply chain party to VC will be resulted as an instance of a personalized VM in the cloud with synchronization of both on-cloud and off-cloud supply chain data (such as from the ERP-IS). Such instance will be destroyed along with data for and in the session. We must also look into inter-cloud interoperability and synchronicity. More importantly, how would, e.g., a private cloud (say, a supplier with ezTrack) gives access to a request from a party in a community cloud. We understand that this can be leveraged with the ReBAC model (Relationship-Based Access Control model, based loosely from the Role-based Access Control model) that we have developed. In principles, we argue that the conceptualization of VC is preliminary ready for reference and some realization (or verification as in design science terminology) of VC can be articulated at the technological level.