I have came across a term Data Replication framework but quite difficult to grasp its concepts. I came across when I tried studying about the architecture of Instagram. Although this might be a broad concept, I would like to learn much about this. Explanations appreciated.
Would keep this thread just for learning purposes.
Related
I am a beginner in machine learning. I want to build a model for finding trending feeds like Instagram.
Please suggest which model is recommended for the same.
I will suggest you to choose these modeling frameworks like Modeling choices, Data freshness trading, and Novelty effect, Experimentation (A/B) Small Effects, Impact, and Scientific Method, Normalization, Iteration Speed — Offline Analysis, Value Modeling, and Parting Thoughts.
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Which of the following optimisation methods can't be done in an optimisation software such as CPLEX? Why not?
Dynamic programming
Integer programming
Combinatorial optimisation
Nonlinear programming
Graph theory
Precedence diagram method
Simulation
Queueing theory
Can anyone point me in the right direction? I didn't find too much information regarding the limitations of CPLEX on the IBM website.
Thank you!
That's kind-of a big shopping list, and most of the things on it are not optimisation methods.
For sure CPLEX does integer programming, non-linear programming (just quadratic, SOCP, and similar but not general non-linear) and combinatoric optimisation out of the box.
It is usually possible to re-cast things like DP as MILP models, but will obviously require a bit of work. Lots of MILP models are also based on graphs, so yes it is certainly possible to solve a lot of graph problems using a MILP solver such as CPLEX.
Looking wider at topics like simulation, then that is quite a different approach. Simulation really is NOT an optimisation method, but it can be used alongside optimisation to get extra insights which may be useful in a business context. Might be used for example to discover some empirical relationships that could be used in an optimisation model by CPLEX.
The same can probably also be said for things like queuing theory, precedence, etc. Basically, use CPLEX as an optimisation tool to solve part or all of your problem once you have structured and analysed it via one of these other approaches.
Hope that helps.
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.
Everyone knows about some relevant statistics about positive impact of using test/behavior driven development in real projects. I know statistics can be very misleading, but it would be nice to see something like:
"when started using TDD, we rose productivity and reduced bugs introduction by XY %...".
It would be really nice to show this numbers to managers/customers, when explaining need of writing tests (there are still some people thinking we don't have time for this...)
Thanks
I have collected the following resources so far:
Realizing quality improvement through test driven development: results and experiences of four industrial teams (Microsoft Research):
http://research.microsoft.com/en-us/groups/ese/nagappan_tdd.pdf
resp:
http://www.springerlink.com/content/q91566748q234325/?p=7fd98b01480f49e2925f36393c999a72&pi=3
Test driven development: empirical body of evidence (ITEA):
http://www.agile-itea.org/public/deliverables/ITEA-AGILE-D2.7_v1.0.pdf
A Longitudinal Study of the Use of a Test-Driven Development Practice in Industry (IBM):
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.104.6319&rep=rep1&type=pdf
Evaluating Pair Programming with Respect to System Complexity and Programmer Expertise (IEEE):
http://simula.no/research/se/publications/Arisholm.2006.2/simula_pdf_file
There is a discussion on InfoQ:
http://www.infoq.com/news/2009/03/TDD-Improves-Quality
Also check out this question:
Evidence based studies on the topic of best programming practices?
I am interested in studies and papers detailing trials that explore the evidence for different development practices in object-oriented languages. I am particularly keen on studies that measure productivity or consider the influence of modern IDEs. Can you point recommend any good resources for this? Has much work been done in this area of late?
For better or worse, empirically-driven productivity metrics are synonymous with Agile these days.
One that looks interesting for (shockingly) the agile research paper list
http://www.agilealliance.org/index.php/download_file/view/18/
It appears as though this is an ongoing research area.