How are they different technologically and in terms of practical usage (e.g. different types of datasets available, etc)?
Researched around but can't find a clear answer.
For a high-level comparison, B-PIPE is just a data stream and SAPI is a traditional API. Both B-PIPE and SAPI give access to the same data that powers Bloomberg Terminal but that is effectively all that B-PIPE does whereas SAPI provides a more diverse interface for building out more robust applications. If all you need is the Bloomberg Terminal data stream, then B-PIPE is the more economical choice. However, if you need additional features then B-PIPE will not do but SAPI may suffice, depending on what you need.
Here is the developer manual for a more in-depth look at the two technologies.
Related
This is more of a general type of question. There are multiple ways in which one can develop API's to exchange data. For example:
Python Flask, Apache Camel, Node.js, etc
And using Testing tools like:
SoapUI, Postman, Swagger editor.
My question is what are the pros and cons of developing different types of API at Enterprise level to handle heavy load, is Scalable and Future proof
It really depends on what you are doing.
Is the system growing in number of transactions, size of transactions, number of clients, complexity of request or what?
Can the requests be done in parallel or are there shared resources?
Are requests coming from a variety of locations or networks?
How much are you willing to spend?
All are important considerations.
Does needing just a single word voice recognition reduce the complexity of the task enough to be able to fully perform voice recognition processing offline, on an iOS or Android smartphone? (E.g., could a reasonably accurate counter for the number of times that a single, pre-programmed word was spoken while the microphone is active be developed to work offline on a standard iOS or Android smartphone?).
I've found plenty of tools and examples capturing voice and sending it to an online service (e.g., the Google cloud voice-to-text), but does the single-word focus reduce the complexity enough for the recognition to be doable offline today? If so, do you have any libraries to suggest or where would you start?
Cloud services are good for various reasons relating to your question:
It makes deployment of new versions of the algorithm (which happen much more frequently than most people realize) a lot easier
It allows the developer to collect your data and use it in future algorithm development (or whatever they please)
From a practical standpoint, most deployed models (at least the effective ones) can be quite large and take up quite a bit of space on a mobile device.
In addition to the above, I don't think that the singular word focus changes much, if anything. The model has to not just account for words, but also for the different ways those words can be said (volume, tone, accents, inflection, etc, etc).
So what you are asking can be done but there's also good reasons why it's on the cloud.
I am a very beginner in software and I am asking or a direction to proceed for research technologies to build my app. I am having just an idea for the app. I am trying to build something like zomato but different services. The idea of location based system is similar. I searched online and came to know about GIS systems. But while researching further, it seems I've to create a map all together. This feels redundant to build as we have api of google maps.
But can i use this api to build a system "ON" it????
Any tutorials or some direction in this direction would be helpful.
Also what is difference between GIS and gps based apps.
As you see, I am not very clear in the fundamentals of the GIS and GPS based apps
Thanks for the help
Regarding Android, you have almost all you need by combining the platform API and the comprehensive Google Maps Android API. Regarding the later, it's actually a matter of opting by convenience and possibly paying a licence fee to Google, versus developing your own solutions of aggregating free or cheaper services from elsewhere.
Most problems solved by apps are not the same problems solved by classical GIS software, since the former are more consumer-oriented (using public transportation, navigating a route, planning a trip, finding a nearby restaurant), and the later are more specialist-oriented, typically solving larger-scale and more technical issues (detecting regions with flood risk, monitoring deforestation, calculating volumes of terrain to be bulldozed, etc.)
You should not, IMO, be discouraged by the seemingly hard technical concepts of geography and map making. Your best bet is to have a clear vision of what actual problems you app should be solving, and study the geography topics gradually, as the need arises.
A bit of consideration on your question about GIS:
If it were created today, the GIS acronym would mean any software dealing with geographic data, be it a mobile app or a workstation software suite destined to specialized professional use.
But when it was created, the term meant almost exclusively the later sense, and so it has a lot of tradition and cultural legacy to it - which is of couse not always a good thing. Specifically (at least in my experience), it seems to me the jargon and concepts used by the classic GIS community are a bit impenetrable to the newcomer, specially if she comes from the software-development field instead of the geo-sciences field.
But geographic information availability has gone from scarcity to overwhelming abundance, and so have its enabling technologies: GPS satellites, mobile computing and mobile connectivity.
I've just started work at a new company who have a datawarehouse that uses some bizzare proprietary ETL built in PHP.
I'm looking for arguments as to why its worth the investment to move to a standard system such as SSIS or infomatica or something. The primary reasons I have at the moment are:
A wider and more diverse community of developers available for contract work, replacements etc.
A large online knowledge base/support networks
Ongoing updates and support will be better
What are other good high level arguments to bring a little standardisation in :)
The only real disadvantage is that a lot of the data sources are web apis returning individual row-by-row records which are more easily looped through with PHP as opposed to standard ETL.
Here are some more:
Simplifies development and deployment process.
Easy to debug and incorporate changes. Would reduce maintenance and enhancement costs.
Industry standard ETL tools perform better on large volume of data as they use various techniques like, grid computing, parallel processing, partitioning etc.
Can support many types for data as source or target. Less impact if source or target systems are migrated to a different data store.
Codes are re-usable. Same component of code can be used in multiple processes.
I am trying to use semantic technologies in IOT. From the last two months I am doing literature survey and during this time I came to know some of the tools required like (protege, Apache Jena). Now along with reading papers I want to play with semantic techniques like annotation, linking data etc so that I can get the better understanding of the concepts involved. For the same I have put the roadmap as:
Collect data manually (using sensors) or use some data set already on the web.
Annotate the dataset and possibly use ontology (not sure)
Apply open linking data principles
I am not sure whether this road map is correct or not. I am asking for suggestions in following points
Is this roadmap correct?
How should I approach for steps 2 and 3. In other words which tools should I use for these steps?
Hope you guys can help me in finding a proper way for handling this issue. Thanks
Semantics and IoT (or semantic sensor web [1]) is a hot topic. Congratulations that you choose a interesting and worth pursuing research topic.
In my opinion, your three steps approach looks good. I would recommend you to do a quick prototype so you can learn the possible challenges early.
In addition to the implementation technologies (Portege, etc.), there are some important works might be useful for you:
Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE). [2] It is an important work for sharing and exchanging sensor observation data. Many large organizations (NOAA, NASA, NRCan, AAFC, ESA, etc.) have adopted this standard. This standard has defined a conceptual data model/ontology (O&M, ISO 19156). Note: this is a very comprehensive standard, hence it's very BIG and can be time consuming to read. I recommend to read #2 mentioned below.
OGC SensorThings API (http://ogc-iot.github.io/ogc-iot-api/), a IoT cloud API standard based on the OGC SWE. This might be most relevant to you. It is a light-weight protocol of the SWE family, and designed specifically for IoT. Some early research work has been done to use JSON-LD to annotate SensorThings.
W3C Spatial Data on Web (http://www.w3.org/2015/spatial/wiki/Main_Page). It is an on-going joint work between W3C and OGC. Part of the goal is to mature SSN (Semantic Sensor Network) ontology. Once it's ready, the new SSN can be used to annotate SensorThings API for example. A work worth to monitor.
[1] Sheth, Amit, Cory Henson, and Satya S. Sahoo. "Semantic sensor web." Internet Computing, IEEE 12.4 (2008): 78-83.
[2] Bröring, Arne, et al. "New generation sensor web enablement." Sensors 11.3 (2011): 2652-2699.