Combining modelling and DSS for effective business analytics


By Peter Keenan, Associate Professor at Centre for Business Analytics, University College Dublin.

Modelling has always taken advantage of the power of computing, and the enormous increase in computing power has allowed the use of ever more sophisticated modelling techniques. However, despite this growing computer power, modelling approaches have often only been capable of solving simplified versions of problems. While this simplification makes the problems computationally tractable, these simplifications often make theoretically efficient solutions of little value in the real world. To provide effective solutions for Business Analytics, we are interested not only in the theoretical power of the solution technique, but also in its actual fit with the real business problem. Faster computers will simply lead to an inappropriate answer in less time, if the problem modelled is not a useful representation of the one we actually want to solve.

What effective business analytics requires is the application of realistic modelling to appropriate data and the presentation of the output in a form useful to business decision-makers. This wish to provide a system under the control of decision-makers was the original inspiration for Decision Support Systems (DSS), which combined models, databases and user friendly interfaces to support decisions. The idea of DSS was to allow users, business decision-makers who understood the decision, interact with and control the modelling process and understand the solution presented. The greatest benefit of DSS is obtained when the scope of the problem is sufficiently complex for the decision-maker to have problems fully understanding it, and so where modelling can assist with problem solving. DSS can support such problems where the problem and solution can also be effectively visualised, allowing the user effectively intervene in the solution. Such user intervention is particularly valuable where the modelling approach does not include all of the practical constraints of interest to the decision-maker. Transportation applications were an early area of DSS application and problems like vehicle routing are both diverse and complex. However, the user can visualise the problem on a map interface and intervene in the solution, for instance by moving a customer from one route to another. There is a very wide range of decisions and so a very diverse range of DSS applications in all business domains.

DSS first emerged in the 1970s, when databases and interactive interfaces first became available. As technology has evolved, DSS has taken advantage of new developments to improve decision support. These developments have included high resolution graphics, mobile devices, and virtual reality interfaces. New technology means that better decision support can be provided. For instance, in the vehicle routing field we now have comprehensive spatial databases of road networks, geographic information system (GIS) software and global positioning system (GPS) devices to record the position of vehicles. These can be integrated in a modern DSS to enhance the value of long established modelling approaches so that solution quality is improved by more realistic modelling. If analytics modelling is to be an applied science, rather than a purely theoretical exercise, then models must be embedded in systems that actually facilitate real decisions; this is the field of DSS.

Optimization on environmental models: Formal co-simulation to find a sustainable design


By Pau Fonseca i Casas, professor, Polytechnical University of Catalonia.

Environmental simulation models are difficult to handle due to the inherent complexity of the elements involved on the analyzed systems. How we can deal with the complexity of those systems? How can we work with the diversity of the languages and terminologies used by different experts? How can we allow an extensive Validation and Verification of such models? The use of formal languages and co-simulation techniques allow the combination of several simulation and optimization techniques in a single simulation engine, helping to improve system design concerning environmental issues. New methodologies are emerging, allowing to obtain optimal or sub-optimal solutions for these kind of systems without concessions on the necessary Validation and Verification processes. On the specific area of Smart Cities and Building energy management, different frameworks (e.g. NECADA) use this methodology to improve the sustainability in urban areas or buildings. This allows to combine IoT, Data Mining, Optimization, Simulation and other techniques through the formalized model.

« A formalized model respresents the structure and the behaviour of the model in a clear and unambiguous way. »

NECADA is a hybrid infrastructure that supports the execution of simulation models on cloud, cluster or desktop environments. The NECADA framework can optimize complete life cycles of buildings or urban areas, ranging from the design, building, actual use and the final rehabilitation and deconstruction processes, making the design of Net Zero Energy Buildings possible. The model implemented on NECADA considers the environmental directives and international rules in the design process (CEN TC 350) to optimize the behaviour of buildings or cities from the point of view of sustainability. It is defined using Specification and Description Language (SDL), a formal language from the Telecommunication Standardization Sector (from the UML family) with enhanced graphical capabilities, great expressiveness and easy to combine with other engineering languages. SDLPS, our software that understands SDL, performs an automatic simulation from this graphical representation of the model. No implementation is needed leading to a simplified verification, modification and expansion of the model. The simulation model definition is so simple that it can be accomplished and understood by all the members of the team, simplifying validation processes. Finally, co-simulation techniques are applied to state of the art calculus engines, like EnergyPlus, Trnsys, etc., using building information models to represent the complete building or area life cycles. Thanks to this architecture we can use NECADA to analyze thousands of different alternatives, using the power of super-computing or the precision of optimization algorithms to find the optimal solution for your building or urban area.

Intelligent support system for resilient port terminal

BMelian_MMoreno_postBy Belén Melián-Batista and J. Marcos Moreno-Vega, associate professors, University of La Laguna.

International freight plays an important role in the development and consolidation of important economic sectors such as trade, industry and tourism. The ports are critical infrastructures within multimodal transport networks that integrate maritime and terrestrial environments. They are also vulnerable to unforeseen events caused by nature or by deliberate or accidental human actions. The consequences range from the reduction of the productivity up to the temporary closure of the port.

In order to manage the risks and improve the robustness of the system, mechanisms that increase the resilience of these infrastructures are important. Resilience is the ability to withstand and recover intelligently from eventualities, facilitating the development of activities in the shortest possible time.

« The design, implementation and validation of a support system for the intelligent management to ensure the resilience of a port container terminal becomes a key issue »

Port operators must have tools to help them identify what unforeseen events can affect the port and what are their consequences. Moreover, they must design strategies to minimize its occurrence (proactive approach). If despite this fact, the event occurs, they should enable protocols and adopt decisions to repair the situation by minimizing the response time or the impact of such an event (reactive action).

For proactive management, the use of simulation tools with which to assess the impact of unexpected events on the overall system performance is mandatory. For reactive management, the design of intelligent replanning algorithms that allow us to restore the performance of the port to pre-incident levels is required.

Using statistics in the smart way: comparing impact factors and h-indices across disciplines


By Rubén Ruiz, professor, Department of Applied Statistics, Operational Research and Quality, UPV; & Ángel A. Juan, associate professor, Computer Science Department, IN3 – UOC.

Data is everywhere. In the age of the internet we use data, computers, and statistics to analyze, measure, and compare everything: different strategies, policies, systems, processes, and even people. This allows us to be more informed and as a consequence, make better decisions. In a word, it makes us wiser than ever.

However, the ease of access to data and computers can also have some drawbacks if we are not cautious. Sometimes it is too easy to succumb to the temptation to compare apples with oranges, and consider the results of these comparisons as valid just because we are using some fancy math formulas, lots of Internet-based data sources, or powerful computers. Of course, extracting conclusions after comparing apples with oranges is a practice we should try to avoid.

In academics and research, this is what happens when we compare the impact factors (IF) of journals that belong to different disciplines. For instance, according to the ISI JCR 2013 Science Edition, the Reliability Engineering & System Safety journal has an IF of 2.048, and it belongs in the first quartile of its subject category ranking (Operations Research & Management Science).

« Extracting conclusions after comparing apples with oranges is a practice we should try to avoid »

However, whist having a similar IF (2.055), the Neuroscience Letters journal is located in the third quartile of its subject category (Neurosciences). To give an even more extreme example: Advanced Mathematics has an IF of 1.353 and belongs in the first quartile of its category (Mathematics), while Methods in Cell Biology, with an “equivalent” IF of 1.440, belongs in the fourth quartile of its category (Cell Biology).

Therefore, it is not reasonable to compare IFs belonging to different subject categories, at least not without applying some statistical adjustment to the index (e.g., normalizing by the IF range, etc.). Of course, it is even worse when somebody makes a direct comparison between two indices that use completely different criteria, e.g., the IF and the SCImago Journal Rank (SJR). Both are positive numbers used to establish journal rankings, but they are based on different mathematical formulas.

And what happens when comparing the h-indices of authors belonging to different disciplines? Recall that the h-index of an author is a given number “h” so that h of his or her Np papers have at least h citations each and the other (Np – h) papers have less than or equal to h citations each. Np is the number of papers published over ten years. So an author with an h-index of 10 has published at least 10 papers with at least 10 citations (or more).

Similarly to IF, as journals in some disciplines (e.g., Neurosciences) tend to receive many more citations than journals in other disciplines (e.g., Mathematics), it is obvious that a direct comparison of h-indices across disciplines will cause us the same problem as before. It is easy to argue that the h-index is a preferable measure when simply comparing the total papers published or the total received citations. However, it is not an indicator that one can use to compare between disciplines easily.

« Pure bibliometric indicators must be supplemented with an article-based assessment and peer-review of the past achievements of an individual »

As suggested in the paper by Ciriminna and Pagliaro1, it is good to use an h-index as a bibliometric indicator but its use should be made wisely, for example by doing an age-normalization or by considering the year of the first publication. Complementing the h-index with the SNIP (Source Normalized Index per Paper) or the aforementioned SJR would give a much richer picture.

In any case, there is a growing move towards the view that for greater accuracy, pure bibliometric indicators must be supplemented with an article-based assessment and peer-review of the past achievements of an individual. After all, it is obvious that no single easy to understand, easy to calculate metric can fully capture the rich and complex nature of the scholarly contribution of researchers to their disciplines, especially considering the rich and varied nature of these disciplines. Many forms and facets of scholarly achievement should be considered.

In summary, both the IF and the h-index are useful indicators, but they should be statistically adjusted and used with caution, especially when used to contrast journals or authors across disciplines. Despite this quite obvious conclusion, it is surprising to see how many times brilliant academics, researchers, and managers are still comparing apples with oranges. Shouldn’t we use statistics in a smarter way?

Further reading:


1. Ciriminna, R. and Plagiaro, M., (2013). On the use of the h-index in evaluating chemical research. Chemistry Central, 7:132.

Bridging the gap between academy and industry: applied Operations Research/Analytics


By Álvaro García Sánchez, professor, Technical School of Industrial Engineering, Technical University of Madrid.

Optimization problems in real world systems are a huge challenge and are a source of enjoyment and effort for Operation Research/Analytics (OR/A) modelers as well of satisfaction when delivering good quality solutions. Nevertheless, other compelling challenges go along with devising a good solving strategy itself during a project where there is a customer.

A first challenge is to gain the credibility from the manager who is running a system that may me improved thought OR/A models. When there is a chance for modeling a problem and gaining effectiveness and efficiency in some way, prior to modeling itself, first we need to prove or inspire trust in the incumbent manager. Past failures, the gap between the practitioners and the academic worlds and so many other reasons may be stoppers.

The good news is that, once this barrier is overcome, the floodgates are opened for an ongoing improvement process getting more and more from increasingly comprehensive models, more valid, etc. In this sense, projects with a reduced scope can be a great approach for the managers to have a taste of what can be gained from OR/A and from modelers to learn more about the problem, and be in better conditions for having an estimate in terms of time and money of how the whole problem can be addressed.

« Projects with a reduced scope can be a great approach for the managers to have a taste of what can be gained from operations research/analytics »

Another challenge is getting to know the actual problem, so that it can be described in terms of a set of requirements. The modeler has to make a great effort to talk the manager’s language and distill what is relevant from what it is not. A typical issue appears when the client suggests some rules that are normally applied to come up with a solution to their everyday problem.

For example, since a particular process in a factory does not always require a night-scheduled worker, although nothing forbids scheduling that task during any other time of the day. This is not an actual requirement, but something that turns the problem into a tractable one for the scheduler. The modeler needs to know what an actual requirement is and what is not, not arrogantly but humbly, since a lot of knowledge is typically within the mind of some people and cannot be found in a structured document.

Other aspects may also be a source of endeavor, such as gathering and treating raw relevant data, integrating models with existing information systems, devising usable interfaces or managing out of scope demands during the development of the project.

Over the last years, the OR/A world has gained experiences in accomplishing successful projects not only in strictly mathematical terms, but also in the above mentioned key aspects. Thus, practitioners are in better conditions to rely in optimization and simulation tools and techniques for better managing their system. A time of closer cooperation and richer projects lies ahead of us.

Convergence of technologies in analytic databases


By Jesús E. Gabaldón, analytics manager, Accenture Centre of Excellence.

In recent years, analytic databases have become crucial in many areas since they allow to provide the necessary workforce in sectors like social media, marketing, Internet of Things, among many others. For instance, everyday situations like sharing information in Twitter or Facebook would not be possible without this technology.

In this area, Massively Parallel Processing (MPP) as well as HDFS and MapReduce architectures have evolved separately most of the time, and both of them are still running on different tracks. However, each one is serving different purposes. Hadoop has a long record in high performance computing but it easily becomes an inaccurate and slow tool for certain tasks. There are some SQL-on-Hadoop databases but they are still immature and quite SQL unfriendly.

« Strengthening ties between universities and private sector is absolutely necessary for fulfilling the heavy demand of the business sector »

Several private and open source development lines are running in parallel. IBM, Pivotal and Apache Foundation are permanently extending the limits of performance in all directions: in-memory, MPP and Hadoop. However, MPP databases run a completely different strategy, probably the most powerful one for strongly demanding advanced analytic purposes. In any case, most of all are progressively relying on in-memory capabilities while caching more types of operations on progressively less expensive SSD supports.

MPP analytic databases are still less known than Hadoop but it is expected that they will turn soon into a strategic area, having a strong impact on highly process demanding tasks in sectors like digital marketing, banking and security. During the next years we will likely attend to a quick evolution of database and analytic technologies fuelled by the strong business demand.

However, this demand is not only requested on hardware performance but on technology development and integration, which often suffers from the scarcity of skilled and well trained professionals. Strengthening ties and establishing collaboration and partnership programs between universities and private sector is still absolutely necessary for fulfilling the heavy demand of the business sector.

Security and privacy in a smarter world


By Anastasios A. Economides, professor, University of Macedonia.

Internet of Things (IoT) is the worldwide Information and Communication Technologies (ICT) infrastructure that will support ubiquitous services among interacting humans, machines, data and applications. The proliferation of connected people, sensors, mobiles and applications is paving the way to IoT. It is expected that about 30 billion devices will be interconnected developing a market of around $ 2 trillion by 2020.

Most companies already use or plan to implement soon an IoT application in various IoT sectors, such as the following:

    • • Smart Healthcare & Wellbeing (e.g. Angel Sensor, Fitbit, Hexoskin, Intraway, Jawbone, Nymi, OnKöl Health Hub, Pebble, Philips Lifeline, Withings, Zebra MotionWorks).
    • • Smart Home & Building (e.g. Belkin, Nest, Neurio, Quirky, Sensorflare, SMA, SmartThings, Vivint, WallyHome, Withings, ZEN Thermostat).
    • • Smart City & Community (e.g. Bigbelly, Bitlock¸ FUKUSHIMA Wheel, Kiunsys, Placemeter, Silver Spring Networks, Waspmote).
    • • Smart Utilities (e.g. Enevo, Mayflower CMS, MeterNet, Osprey Informatics, Paradox, Trilliant).
    • • Smart Environmental Monitoring (e.g. FilesThruTheAir, Fruition Sciences, OnFarm, Semios, Topcon Precision Agriculture).
    • • Smart Car & Transportation (e.g. Audi, CarKnow, Connected Rail, Dash drive smart, Delphi Connect, Ericsson, Libelium, Logitrac, PowerFleet).
    • Smart Industry & Services (e.g. Argon Underground Mining Safety, Condeco Sense, DAQRI’s Smart Helmet, Numerex, Perch).

An amazing amount of confidential information will be collected, communicated, stored and processed by third parties. However, the consequences regarding security and privacy are unknown.

«Through IoT services, an amazing amount of confidential information will be collected, communicated, stored and processed by third parties»

Few companies admit to be prepared for tackling the IoT security challenges. A recent survey found that only 30% of IT professionals believe their company has the technology necessary to adequately evaluate the security of IoT devices. Also, 20% of the responders state that they have “no visibility” into current protection levels.

Security researchers from Hewlett-Packard performed a security audit of 10 popular Internet-connected devices: TVs, webcams, home thermostats, remote power outlets, sprinkler controllers, hubs for controlling multiple devices, door locks, home alarms, scales and garages. On average, they identified 25 vulnerabilities per device. Six out of the 10 devices that did not use encryption when downloading software updates. 70% of the devices used unencrypted network services and transmitted credentials in plain text. Also, 90% of the devices collected at least one piece of personal information via the device, the cloud, or its mobile application.

Last year, Team Cymru’s researchers identified over 300,000 home routers that have been attacked. This large scale attack was altering the Domain Name Server (DNS) settings of these compromised routers to redirect the victims’ DNS requests and subsequently replace the intended answers with Internet Protocol (IP) addresses and domains controlled by the attackers.

«Although many security officers believe their security is optimized and effective, in reality, this is not true»

Cisco just announced the results of its Security Capabilities Benchmark Survey among Chief Information Security Officers (CISOs) and Security Operations (SecOps) executives at 1700 companies in nine countries. Only 10% of Internet Explorer users run the latest version. Less than 50% of respondents use standard tools such as patching and configuration to help prevent security breaches and ensure that they are running the latest versions. Although many security officers believe their security is optimized and effective, in reality, this is not true.

It becomes apparent that society is not well prepared to face the security and privacy risks regarding IoT. Further security actions should be taken by ICT companies, businesses, public authorities as well individuals. Finally, I would like to invite you to a workshop that will discuss about those relevant issues in a few months: the IoT/CPS-Security 2015, which will be held in London from 8 to 12 June 2015.

Business Analytics: Can’t we do it better?


By Angel A. Juan, associate professor at UOC–IN3, & Helena R. Lourenço, associate professor at Universitat Pompeu Fabra.

Descriptive Business Analytics (DBA) refers to the pre-processing and processing of historical data gathered by companies in order to describe the real business context, generate information, and make rational decisions from the acquired knowledge.

Traditionally, DBA makes use of programming languages and software to “crunch” the raw data (e.g., R, Python, etc.), database concepts, and data analysis methods. Despite the fact that DBA is probably the most employed type of business analytics among practitioners in enterprises and institutions, it is not the only one.

In effect, after describing the business real context, one can also benefit from Predictive Business Analytics (PdBA), which relies on the use of time series analysis, regression models, and even machine learning methods in order to forecast the future or predict how some business factors will evolve under uncertainty scenarios. Of course, by adding PdBA to DBA we can make smarter decisions.

«The algorithms used in Prescriptive Business Analytics processes allow managers to “learn from the future”»

Nevertheless, a natural question arises, can’t we do it better? As expected, the answer is positive, and the way to do it is by using Prescriptive Business Analytics (PsBA). PsBA aims at supporting complex decision-making processes in business throughout the use of optimization and simulation algorithms (including metaheuristics and simheuristics). These algorithms allow managers to “learn from the future” (by performing what-if analysis), significantly increasing the efficiency of business processes and systems, reducing operations costs and, at the end of the day, raising companies’ benefits.

Why then companies and institutions are not massively using PsBA yet? Well, on the one hand PsBA methods require specific analytic skills and knowledge, which is not always available in all organizations. On the other hand, PsBA requires time to: (a) model real-life business processes and systems; and (b) develop the algorithms to efficiently deal with these issues. However, companies and governments in some of the most evolved countries (e.g., USA, Germany, UK) do frequently rely in PsBA algorithms to go smarter and gain competitive advantage in a global economy. Shouldn’t we — both organizations and analytics practitioners — be smarter too?

Note: With this post we — the people behind this blog — would like to celebrate the 2nd anniversary of this exciting project. During these 24 months, the blog has gathered nearly 60 responses to the main question, coming from different knowledge areas and backgrounds. We deeply thank both all contributors for their time devoted to this project and all the readers that have been checking the blog month after month, thus giving a sense to it.

Making Media Smarter


By Johanna Blakley, Managing Director & Director of Research, The Norman Lear Center, University of Southern California.

The entertainment industry is notorious for adjusting its numbers to service an often inscrutable bottom line. And all of us — including everyone who variously produces or consumes media content — have been ill-served by cookie-cutter audience segmentation techniques and panel-based research methods that cannot account for what’s happening in the “long tail” of our global cultural economy.

The insidious audience segmentation techniques that valorize age, race, gender and income over every other facet of human identity have contributed to a media system rife with stereotypes about how humans tick. The tremendous data sets emerging from social media networks offer us the opportunity to understand ourselves, and our engagement with media, in a far more nuanced way (check out my TED talk about this).

«We want media makers to have a far more sophisticated and detailed understanding of their audiences needs, values and taste»

For the last two years, I have been co-principal investigator on a major new research initiative at the University of Southern California: the Media Impact Project is a global hub for the best research on measuring the impact of media. Supported by the Gates, Knight and Open Society Foundations, I’m optimistic that we can help make media more accountable to audiences and contribute to a better understanding of the role that media plays in people’s lives.

Our vision here? Ultimately, we want media makers to have the resources to make data-driven decisions. Rather than depending on their “gut” alone, we encourage them to grapple with meaningful feedback information that demonstrates how real people have engaged with their work and what effects that interaction has produced.

We also want media makers to have a far more sophisticated and detailed understanding of their audience’s needs, values and taste. For me, it’s really an issue of respect. Our media environment should be respectful and responsive to the needs of global audiences, rather than painfully engineered to fit stereotypical notions of the interests of a few prized demographic groups.

Note: Interested in media impact? Follow the Media Impact Project on Twitter at @mediametrics and find us on Facebook.

To be smart enough you need… Engagement!


By José Carlos Cortizo, CMO and co-founder of BrainSINS, and co-founder of Gamification World Congress.

When we talk about technology, we forget very often that technology is… just technology. Current technological capabilities were unimaginable centuries or even a few decades ago, nevertheless, many problems that we expected to overcome with technology years ago, remain unsolved.

We must not forget that many of the problems we want to solve with technology are problems related to human behaviour. We say we want a greener world, because our society creates a tremendous amount of pollution and waste. We think that the Smart Cities concept will radically change today’s cities, while forgetting the challenge this will pose for the inhabitants of these cities when interacting with the technologies involved. From a technological point of view, wearable devices offer a world of endless possibilities, but we are still not sure how they will be accepted by society. In fact, Google Glass, which is still a prototype, has generated significant controversy regarding privacy among other aspects.

«The principal challenges posed by any disruptive technology come from the human being»

What is the use of putting more recycling containers in a city if we are not able to increase the proportion of people that use them properly? What is the use of online education campuses if educational models are still derived from the previous century? The principal challenges posed by any disruptive technology come from the human being. Reluctant to change, non-participative if something affects us very directly, and living a lifestyle that is ever more individualistic, self-centred and less focused on the communities we are a part of.

For all these reasons, and many more, some time ago I reached the conclusion that what we need is the engagement of all members of society. We need to focus on concepts more than specific technologies, on interaction with human beings more than with the devices that enable these interactions. We need to get people feeling that they are really participating in any new initiative, more than just thinking that as they are at the cutting edge of technology, this will automatically change their lives.

Therefore, in the last few years, I have focused a significant part of my work on the world of gamification, which teaches us to motivate people, to connect more frequently and more deeply with our communities and workers. And which has the ability to generate the engagement necessary to produce the changes society needs. Examples such as the Bottle Bank Arcade teach us how to introduce low technology elements focused on making people play while carrying out an activity that is useful for society (bottle recycling in this case), enabling the participants in these activities to become aware of how necessary they are.

«Gamification teaches us to motivate people, to connect more frequently and more deeply with our communities and workers»

In the United Kingdom, the Department for Work and Pensions launched a programme called Idea Street, like a suggestions box for the agency, rewarding workers for new ideas. This programme has been a success, proving that the introduction of small game elements creates extra motivation to involve employees in decision-making. This has already produced business results in the medium term, as it is estimated that in 2014 more than 30 million pounds were saved thanks to the application of some of the disruptive ideas from Idea Street.

Last year, in the Gamification World Congress 2014, we also saw many examples of the application of game mechanics in different fields such as education, health, marketing, sales force motivation, etc. Some of these examples are based on complex technologies, but many others can achieve results with technologies as accessible as “paper and pen”, “post-its”, and other elements accessible by everyone.

In short, technology plays an increasingly important role in our lives, helping us to achieve more intelligent societies. But we must not forget that people are what really change the world, and if any technology is to be successful, it must involve the people who are the “target audience” of the technology in question. Focusing more on people and less on technology will achieve more and better results.