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• Data Modeling Resources

  - by Dejan Sarka

  You can find many different data modeling resources. It is impossible to list all of them. I selected only the most valuable ones for me, and, of course, the ones I contributed to. Books Chris J. Date: An Introduction to Database Systems – IMO a “must” to understand the relational model correctly. Terry Halpin, Tony Morgan: Information Modeling and Relational Databases – meet the object-role modeling leaders. Chris J. Date, Nikos Lorentzos and Hugh Darwen: Time and Relational Theory, Second Edition: Temporal Databases in the Relational Model and SQL – all theory needed to manage temporal data. Louis Davidson, Jessica M. Moss: Pro SQL Server 2012 Relational Database Design and Implementation – the best SQL Server focused data modeling book I know by two of my friends. Dejan Sarka, et al.: MCITP Self-Paced Training Kit (Exam 70-441): Designing Database Solutions by Using Microsoft® SQL Server™ 2005 – SQL Server 2005 data modeling training kit. Most of the text is still valid for SQL Server 2008, 2008 R2, 2012 and 2014. Itzik Ben-Gan, Lubor Kollar, Dejan Sarka, Steve Kass: Inside Microsoft SQL Server 2008 T-SQL Querying – Steve wrote a chapter with mathematical background, and I added a chapter with theoretical introduction to the relational model. Itzik Ben-Gan, Dejan Sarka, Roger Wolter, Greg Low, Ed Katibah, Isaac Kunen: Inside Microsoft SQL Server 2008 T-SQL Programming – I added three chapters with theoretical introduction and practical solutions for the user-defined data types, dynamic schema and temporal data. Dejan Sarka, Matija Lah, Grega Jerkic: Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft SQL Server 2012 – my first two chapters are about data warehouse design and implementation. Courses Data Modeling Essentials – I wrote a 3-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Logical and Physical Modeling for Analytical Applications – online course I wrote for Pluralsight. Working with Temporal data in SQL Server – my latest Pluralsight course, where besides theory and implementation I introduce many original ways how to optimize temporal queries. Forthcoming presentations SQL Bits 12, July 17th – 19th, Telford, UK – I have a full-day pre-conference seminar Advanced Data Modeling Topics there.

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• 5 Ways to Determine Mobile Location

  - by David Dorf

  In my previous post, I mentioned the importance of determining the location of a consumer using their mobile phone.  Retailers can track anonymous mobile phones to determine traffic patterns both inside and outside their stores.  And with consumers' permission, retailers can send location-aware offers to mobile phones; for example, a coupon for cereal as you walk down that aisle.  When paying with Square, your location is matched with the transaction.  So there are lots of reasons for retailers to want to know the location of their customers.  But how is it done? I thought I'd dive a little deeper on that topic and consider the approaches to determining location. 1. Tower Triangulation By comparing the relative signal strength from multiple antenna towers, a general location of a phone can be roughly determined to an accuracy of 200-1000 meters.  The more towers involved, the more accurate the location. 2. GPS Using Global Positioning Satellites is more accurate than using cell towers, but it takes longer to find the satellites, it uses more battery, and it won't well indoors.  For geo-fencing applications, like those provided by Placecast and Digby, cell towers are often used to determine if the consumer is nearing a "fence" then switches to GPS to determine the actual crossing of the fence. 3. WiFi Triangulation WiFi triangulation is usually more accurate than using towers just because there are so many more WiFi access points (i.e. radios in routers) around. The position of each WiFi AP needs to be recorded in a database and used in the calculations, which is what Skyhook has been doing since 2008.  Another advantage to this method is that works well indoors, although it usually requires additional WiFi beacons to get the accuracy down to 5-10 meters.  Companies like ZuluTime, Aisle411, and PointInside have been perfecting this approach for retailers like Meijer, Walgreens, and HomeDepot. Keep in mind that a mobile phone doesn't have to connect to the WiFi network in order for it to be located.  The WiFi radio in the phone only needs to be on.  Even when not connected, WiFi radios talk to each other to prepare for a possible connection. 4. Hybrid Approaches Naturally the most accurate approach is to combine the approaches described above.  The more available data points, the greater the accuracy.  Companies like ShopKick like to add in acoustic triangulation using the phone's microphone, and NearBuy can use video analytics to increase accuracy. 5. Magnetic Fields The latest approach, and this one is really new, takes a page from the animal kingdom.  As you've probably learned from guys like Marlin Perkins, some animals use the Earth's magnetic fields to navigate.  By recording magnetic variations within a store, then matching those readings with ones from a consumer's phone, location can be accurately determined.  At least that's the approach IndoorAtlas is taking, and the science seems to bear out.  It works well indoors, and doesn't require retailers to purchase any additional hardware.  Keep an eye on this one.

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• How to Waste Your Marketing Budget

  - by Mike Stiles

  Philosophers have long said if you find out where a man’s money is, you’ll know where his heart is. Find out where money in a marketing budget is allocated, and you’ll know how adaptive and ready that company is for the near future. Marketing spends are an investment. Not unlike buying stock, the money is placed in areas the marketer feels will yield the highest return. Good stock pickers know the lay of the land, the sectors, the companies, and trends. Likewise, good marketers should know the media available to them, their audience, what they like & want, what they want their marketing to achieve…and trends. So what are they doing? And how are they doing? A recent eTail report shows nearly half of retailers planned on focusing on SEO, SEM, and site research technologies in the coming months. On the surface, that’s smart. You want people to find you. And you’re willing to let the SEO tail wag the dog and dictate the quality (or lack thereof) of your content such as blogs to make that happen. So search is prioritized well ahead of social, multi-channel initiatives, email, even mobile - despite the undisputed explosive growth and adoption of it by the public. 13% of retailers plan to focus on online video in the next 3 months. 29% said they’d look at it in 6 months. Buying SEO trickery is easy. Attracting and holding an audience with wanted, relevant content…that’s the hard part. So marketers continue to kick the content can down the road. Pretty risky since content can draw and bind customers to you. Asked to look a year ahead, retailers started thinking about CRM systems, customer segmentation, and loyalty, (again well ahead of online video, social and site personalization). What these investors are missing is social is spreading across every function of the enterprise and will be a part of CRM, personalization, loyalty programs, etc. They’re using social for engagement but not for PR, customer service, and sales. Mistake. Allocations are being made seemingly blind to the trends. Even more peculiar are the results of an analysis Mary Meeker of Kleiner Perkins made. She looked at how much time people spend with media types and how marketers are investing in those media. 26% of media consumption is online, marketers spend 22% of their ad budgets there. 10% of media time is spent with mobile, but marketers are spending 1% of their ad budgets there. 7% of media time is spent with print, but (get this) marketers spend 25% of their ad budgets there. It’s like being on Superman’s Bizarro World. Mary adds that of the online spending, most goes to search while spends on content, even ad content, stayed flat. Stock pickers know to buy low and sell high. It means peering with info in hand into the likely future of a stock and making the investment in it before it peaks. Either marketers aren’t believing the data and trends they’re seeing, or they can’t convince higher-ups to acknowledge change and adjust their portfolios accordingly. Follow @mikestilesImage via stock.xchng

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• Increasing speed of python code

  - by Curious2learn

  Hi, I have some python code that has many classes. I used cProfile to find that the total time to run the program is 68 seconds. I found that the following function in a class called Buyers takes about 60 seconds of those 68 seconds. I have to run the program about 100 times, so any increase in speed will help. Can you suggest ways to increase the speed by modifying the code? If you need more information that will help, please let me know. def qtyDemanded(self, timePd, priceVector): '''Returns quantity demanded in period timePd. In addition, also updates the list of customers and non-customers. Inputs: timePd and priceVector Output: count of people for whom priceVector[-1] < utility ''' ## Initialize count of customers to zero ## Set self.customers and self.nonCustomers to empty lists price = priceVector[-1] count = 0 self.customers = [] self.nonCustomers = [] for person in self.people: if person.utility >= price: person.customer = 1 self.customers.append(person) else: person.customer = 0 self.nonCustomers.append(person) return len(self.customers) self.people is a list of person objects. Each person has customer and utility as its attributes. EDIT - responsed added ------------------------------------- Thanks so much for the suggestions. Here is the response to some questions and suggestions people have kindly made. I have not tried them all, but will try others and write back later. (1) @amber - the function is accessed 80,000 times. (2) @gnibbler and others - self.people is a list of Person objects in memory. Not connected to a database. (3) @Hugh Bothwell cumtime taken by the original function - 60.8 s (accessed 80000 times) cumtime taken by the new function with local function aliases as suggested - 56.4 s (accessed 80000 times) (4) @rotoglup and @Martin Thomas I have not tried your solutions yet. I need to check the rest of the code to see the places where I use self.customers before I can make the change of not appending the customers to self.customers list. But I will try this and write back. (5) @TryPyPy - thanks for your kind offer to check the code. Let me first read a little on the suggestions you have made to see if those will be feasible to use. EDIT 2 Some suggested that since I am flagging the customers and noncustomers in the self.people, I should try without creating separate lists of self.customers and self.noncustomers using append. Instead, I should loop over the self.people to find the number of customers. I tried the following code and timed both functions below f_w_append and f_wo_append. I did find that the latter takes less time, but it is still 96% of the time taken by the former. That is, it is a very small increase in the speed. @TryPyPy - The following piece of code is complete enough to check the bottleneck function, in case your offer is still there to check it with other compilers. Thanks again to everyone who replied. import numpy class person(object): def __init__(self, util): self.utility = util self.customer = 0 class population(object): def __init__(self, numpeople): self.people = [] self.cus = [] self.noncus = [] numpy.random.seed(1) utils = numpy.random.uniform(0, 300, numpeople) for u in utils: per = person(u) self.people.append(per) popn = population(300) def f_w_append(): '''Function with append''' P = 75 cus = [] noncus = [] for per in popn.people: if per.utility >= P: per.customer = 1 cus.append(per) else: per.customer = 0 noncus.append(per) return len(cus) def f_wo_append(): '''Function without append''' P = 75 for per in popn.people: if per.utility >= P: per.customer = 1 else: per.customer = 0 numcustomers = 0 for per in popn.people: if per.customer == 1: numcustomers += 1 return numcustomers

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• How can I disable 'output escaping' in minidom

  - by William

  I'm trying to build an xml document from scratch using xml.dom.minidom. Everything was going well until I tried to make a text node with a ® (Registered Trademark) symbol in. My objective is for when I finally hit print mydoc.toxml() this particular node will actually contain a ® symbol. First I tried: import xml.dom.minidom as mdom data = '®' which gives the rather obvious error of: File "C:\src\python\HTMLGen\test2.py", line 3 SyntaxError: Non-ASCII character '\xae' in file C:\src\python\HTMLGen\test2.py on line 3, but no encoding declared; see http://www.python.or g/peps/pep-0263.html for details I have of course also tried changing the encoding of my python script to 'utf-8' using the opening line comment method, but this didn't help. So I thought import xml.dom.minidom as mdom data = '&#174;' #Both accepted xml encodings for registered trademark data = '&reg;' text = mdom.Text() text.data = data print data print text.toxml() But because when I print text.toxml(), the ampersands are being escaped, I get this output: &reg; &amp;reg; My question is, does anybody know of a way that I can force the ampersands not to be escaped in the output, so that I can have my special character reference carry through to the XML document? Basically, for this node, I want print text.toxml() to produce output of &reg; or &#174; in a happy and cooperative way! EDIT 1: By the way, if minidom actually doesn't have this capacity, I am perfectly happy using another module that you can recommend which does. EDIT 2: As Hugh suggested, I tried using data = u'®' (while also using data # -*- coding: utf-8 -*- Python source tags). This almost helped in the sense that it actually caused the ® symbol itself to be outputted to my xml. This is actually not the result I am looking for. As you may have guessed by now (and perhaps I should have specified earlier) this xml document happens to be an HTML page, which needs to work in a browser. So having ® in the document ends up causing rubbish in the browser (® to be precise!). I also tried: data = unichr(174) text.data = data.encode('ascii','xmlcharrefreplace') print text.toxml() But of course this lead to the same origional problem where all that happens is the ampersand gets escaped by .toxml(). My ideal scenario would be some way of escaping the ampersand so that the XML printing function won't "escape" it on my behalf for the document (in other words, achieving my original goal of having &reg; or &#174; appear in the document). Seems like soon I'm going to have to resort to regular expressions! EDIT 2a: Or perhaps not. Seems like getting my html meta information correct <META http-equiv="Content-Type" Content="text/html; charset=UTF-8"> could help, but I'm not sure yet how this fits in with the xml structure...

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