On part 1, I introduced the importance of social network understanding as the socialization of interactions is becoming a new working habit and as such classic control flow perspective analysis does not anymore provide information about how work is done.
On this post, I will explore important points to look for when performing Social Network Analysis (SNA).
Social networks have typically the following properties:
- Emergence: agents that belong to the network interact in an apparently random way. This feeling is amplified if there are many agents and / or there are too many interactions that make difficult to extract patterns. Emergence is all about separating the signal form the noise and make those patterns to emerge.
- Adaptation: enterprises, communities, exist confined in a particular environment that when changes it makes agents to react. Environment can be external, interaction with customers, suppliers, government agencies; influence like the publication of a new law or regulation or competitor movements as they enter in new markets or create new products or services. Environment can also be internal and its related to the way agents interact that is ultimately associated with how business processes were designed, how IT solutions were deployed, culture, hierarchy configuration and formal recognition of authority, just to provide some examples.
- Variety: Ashby, one of the father’s of cybernetics, defined the Law of Requisite Variety “variety absorbs variety, defines the minimum number of states necessary for a controller to control a system of a given number of states.” For an organisation, to be viable it must be capable of coping with the variety (complexity) of the environment in which it operates. Managing complexity is the essence of a manager’s activity. Controlling a situation means being able to deal with its complexity, that is, its variety .
- Connectivity: The way agents are connected and how those connections are aligned with the process type that was designed / being executed and the type of knowledge that is necessary to support operations (more about this alignment here). The existing connections will unveil the emergent patterns that are necessary to identify and understand behaviour under a social point of view (high coupling or loosely coupling between agents or group of agents).
On network types:
Most of the times when people refer to social networks they are expressing their beliefs on community networks like Facebook, subject expert groups like enterprise wikis. Although those are important network types, they do not express the nature of organization operations, because they do not record communication acts expressed on social activity, hence I will only concentrate on Coordination Networks.
A Coordination Network is a network formed by agents related to each other by recorded coordination acts.
Coordination acts are for example, the interchange of emails, tasks as design on enterprise systems or activity streams just to provide some examples. The above definition is an adaptation of  because it does not include the importance of coordination act that is related with the nature of work, rather the connection itself. The former is the important dimension related with business process management and will guide the remaining content.
Coordination acts is meant to be as defined (adapted)  an act to be performed by one agent, directed to other agent that contains an intention (request, promise, question, assertion) and a proposition (something that is or could be the case in the social world). In the intention, the agent proclaims its social attitude with respect to the proposition. In the proposition, the agent proclaims the fact and the associated time the intention is all about, recorded by the system, supporting the definition Coordination networks, which configuration that can ultimately be discovered, patterns emerge, using discovering techniques like for example process mining.
On analysis dimensions:
Social network analysis is not new. Actually, the first studies were done around the 50’s of last century. Its refinement stumbled around:
- Degree distribution: study connection number around a node of the network;
- Clustering: groups with connection density larger than average;
- Community discovery: measures alignment of connections regarding organization hierarchy.
There is an immense list of techniques to analyse each one of the above dimensions, that reflects the high maturity level of each method, but he drawback is that SNA analysed on each dimension alone can induce managers in the wrong direction. For example, studying community discovery can be important, because communities are a collection of individuals who are linked or related by some sort of relation. But carrying the analysis without taking into consideration the content of the conversation (coordination act) that drove the creation of the link is absolutely wrong, because the conversation is all about the way we humans work. I tend to disagree with other points of view from other practitioners that conversation does not matter (probably because they were influenced by Gordon Pask), only the network configuration. Conversation (the process) is the matter of study.
Social networks are self-organizing systems, but there are important patterns that emerge from the nature of the coordination acts that can be identified. Despite there are random factors and the type of patterns presented in most of scientific papers are based on graph theory and tend to be very simple compared with the reality (and hence maybe this is one of the reasons they are not taken seriously) it is the only way, as an abstraction, to understand agent behaviour. Pattern recognition is critical to align process type (from structured to unstructured), knowledge domain (simple to chaotic) and network type (central to loosely coupled). In order words, to infer trends and help humans to interact better regarding the role they play in the process ecosystem. Having said that, I would like to invoke Stafford Beer’s on models: “in general we use models in order to learn something about the thing modelled (unless they are just for fun)” .
Centrality is used to measure degree distribution. Centrality  is described as a process participant, business unit, group (a set of process participants or people) or an enterprise system (do not forget the machines) within the context of a social network. Centrality is also related with discovering the key players in social networks.
Some measures that can be used for Centrality are:
- Degree centrality: calculate how many links a node has regarding the remaining network nodes (commonly called network stars). Higher degree centrality means higher probability of receiving information (but does not mean it drives information flow inside of the network).
- Betweenness: measures the degree witch a process participant controls information flow. They act as brokers. The higher the value, higher is information flow traffic that moves from each node to every other node in the network. The importance of Betweenness in social network analysis is nodes with higher values stop processing coordination acts, will block information flow to run properly.
- Closeness: measures how close a node is isolated in the network compared with other network mode. Nodes with low closeness are able to reach or be reached by most of all other nodes in the network, in other words low closeness means a node is well positioned to receive information early when it has more value. Closeness measure must be supported on time dimension (see reference about the timestamp attribute on the coordination act exemplification), without it, is useless.
- Eigenvector centrality: used to calculate node influence in the network. Higher scores means a node can influence (touch) many other important nodes.
In order o put it all together its worth to consider the following self-explanatory picture :
There is a lot of noise around what is the best measure to perform SNA, as I learned at the User Modelling, Adaptation and Personalization Conference 2011 it’s time to put the mathematical equations aside and practice it’s application.
At this moment of time, there are plenty of ways to measure network centrality, but somehow they neglect that those algorithms are not appropriate regarding the type of business process / information system interaction played. For example, Eigenvector centrality measure is important in unstructured processes, where the path is defined on instance mode and it is necessary to create a team and involve others as the process progress. Once SNA does not analyze the process type, only about agent relation, if applied analyzing a procure to pay process (highly structured process type) it’s useless and can damage results interpretation, because on this case, every agent, every process participant receives and process information basically the same way to achieve the same outcome every same day. Maybe this is the reason why is not yet taken more seriously, because these days the process is all about social interaction and it cannot anymore be analyzed naively taking into consideration the dispersion, complexity and interdependence of relationships, something that can also be applied on IT requirements elicitation or IT system operation , which allows to understand communities interaction in order to support emerging and unique processes under a techno-social systems approach .
 – Design and Diagnosis for Sustainable Organizations – Jose´ Pérez Ríos – Springer – ISBN 9783642223174
 – Large Scale Structure and Dynamics of Complex Networks – Guido Caldarelli; Alessandro Vespignani – World Scientific Publishing – ISBN-139789812706645
 – Enterprise Ontology – Jan Dietz – Springer – ISBN – 3540291695
 – Complex Adaptive Systems Modeling – A multidisciplinary Roadmap – Muaz A Niazi
 – The Brain of the firm – Stafford Beer – Jonh Wiley & Sons – ISBN – 047194839-X
 – Discovering Sets of Key Players in Social Networks – Daniel Ortiz-Arroyo – Springer 2010
 – José L.R. Sousa, Ricardo J. Machado, J.F.F. Mendes. Modeling Organizational Information Systems Using “Complex Networks” Concepts. IEEE Computer Society 2012, ISBN 978-0-7695-4777
On the previous article about A Social Platform Definition, I presented a framework about the elements of such Platform. The following articles I will expand each of the layers. This one is dedicated to the Search and Analysis component.
Before we dig into the component content, I would like to bring some background about its significance.
An important introduction to Social Network Analysis
Last week, I had a meeting with a college headmaster to figure it out if there was alignment between me and the headmaster’s expectations and values regarding how students will be prepared for the forthcoming decades, taking into consideration the shift we are facing regarding work patterns, information overload and technology disruption.
The institution is catholic oriented and have strong roots with the Catholic Church. Let me say that I do not consider myself catholic as by the book definition, but probably I’m more catholic that others that go to the church every day and don’t have ethics and values. This means I did not choose to evaluate the institution because it is linked with my religious beliefs, but because they are the best institution according to the evaluation program that was created by the Portuguese Government some years ago.
During the interaction with the headmaster (a religious person), we talked about two vectors I introduced into the conversation: values and student preparation for the forthcoming decades (how we prepare people to interpret and act on information and how they improve reasoning in the knowledge era). When the headmaster was talking about values, introduced an amazing characteristic from the human being point of view (sorry by the religious background I’m putting into the discussion but I consider that it’s worth for the sake of clarification about social network analysis).
God created humans as a single and unique entity. There are no equal human beings (even perfect twins) and God created animals and all the other living organisms differently that belong to a system (let us call planet earth that belongs to other system called the universe) made by diversity in constant balance and adaptation.
This point of view opens and reinforces the main characteristic that we humans who belong to families, communities, organizations, arrangements that are part of a super system called the universe whose foundations rely on the top of diversity and complexity, not on standardization. Somehow, we keep pushing in into an ordered regime because it is much simpler to understand concepts, interactions and our own existence in an controlled manner rather than in a complex one.
The world is complex and we cannot change that as much we would like to
Ashby’s law teaches us that any system must match the complexity of its elements in an actively and adaptive way to survive and prosper.
In addition, Ashby pointed out other important conclusion: any attempt to limit part of the variety (because it is considered noise by the humans) that constitutes the system will lead that the system will lose the capacity to adapt and lead into implosion. This reflects in the way some business processes cannot respond to exception handling, because the misleading adaptation consists into fighting against the process model rather than adapt to changing executing conditions. If we consider a different organization layer like strategy management, think when external signs are ignored that can lead the organization to bankruptcy or financial loss.
In the social era we are being misleading about what is Social Network Analysis, one of the reasons it is about the semantics, the meaning of Social, broadly understood connected people, but a Social Network is much more than that. In very general terms a Social Network can be described as a graph whose nodes (vertices) identify the elements of the system. The set of connecting links (edges) represents the presence of a relation or interaction among these elements. With such a high level of generality it is easy to perceive that a wide array of systems can be approached within the framework of network theory .
Social Networks can be made of Organizational Units, Business Units, Roles and Functions, Individuals, Data, Technology consumption (what part of the IT solution is used), Technology interaction (how IT solutions communicate), Business Processes, Traffic, Biological, Physics (these last two categories lend so much of its properties to business analysis) etc.
All the networks are self organizing systems, but there are important patterns that can be identified anywhere from the self organization, despite randomness, patterns are critical for humans to understand how data can be transformed into information, that ultimately is transformed into knowledge used to understand the behavior of such networks (see note below).
Self-organization refers to the fact that a system’s structure or organization appears without explicit control or constraints from outside the system. In other words, the organization is intrinsic to the self-organizing system and results from internal constraints or mechanisms, due to local interactions between its components  (that can be put on top of a business process). These interactions are often indirect thanks to the environment. The system dynamics modifies also its environment, and the modifications of the external environment influence in turn the system, but without disturbing the internal mechanisms leading to organization  (think for example social interaction with customers that change the course of the business process, or events during product research and development that makes to alter the characteristics and features). The system evolves dynamically either in time or space, it can maintain a stable form or can show transient phenomena. In fact, from these interactions, emergent properties appear transcending the properties of all the individual sub-units of the system  (and these emergent properties are the ones than be understood using a combined set of discovering techniques like process mining, social network analysis and data mining).
I tend to agree that with argument that looking for patterns into a complex landscape is a waste of time for the reason that into complex domains any attempt to take a snapshot is a distorted version of the reality. Nevertheless, the objective of patterns discovery and understanding is not to predict behavior but to infer trends or in Jason Silva’s words “to understand is to perceive patterns” http://vimeo.com/34182381 .
The objective of Social Network Analysis is not to predict outcomes, but to understand, to construct knowledge around emergence self-organization and adaptation in scenarios like for example decision making or distributed systems that are becoming real enterprise challenges as business complexity and interactions grow exponentially.
Huge amount of data is being recorded today (see image bellow) that allow us to make discovery and analysis of complex interactions. The argument that does exist and it cannot be done only fits in a category like airport security information that typically relies on paper.
The Internet of Things – new infographics – Source: Bosch
On part two, I will explore techniques to analyze social networks.
On Fastcompany’s article: “IBM’s Watson Is Learning Its Way To Saving Lives” is said that “Watson is poised to change the way human beings make decisions about medicine, finance, and work” […] “They believed Watson could help doctors make diagnoses and, even more important, select treatments”. I argue that IT can help humans to process and show data to help humans to make better decisions. Last weekend, a family member stood at a hospital during a day making analysis on what could have been a heart attack. Diagnosis were automatic: they make a 1 minute electrocardiogram (considered insufficient by experts) combined among others with measurement of troponin levels (diagnostic marker for various heart disorders). The results found correlation between the results and the family member was told a cardiologist should immediately see him. When the cardiologist looked to the results he said that there was no correlation at all, the results of the electrocardiogram were insufficient and the troponin level was 1/100 of the danger threshold and was unlikely to raise suddenly. In the end the diagnostic was wrong and the cause of sickness was nervous system. This evidence like many others should make us think as Einstein said: “Information is not knowledge, the only source of knowledge is experience”; I would add information cannot be stored.
 Preliminaries and Basic Definitions in Network Theory – Guido Caldarelli and Alessandro Vespignani – Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science – World Scientific Publishing Company – ISBN 978-9812706645
 Self-Organisation: Paradigms and Applications – Giovanna Di Marzo Serugendo, Noria Foukia, Salima Hassas, Anthony Karageorgos, Soraya Kouadri Mostéfaoui, Omer F. Rana, Mihaela Ulieru, Paul Valckenaers, and Chris Van Aart – Engineering Self-Organising Systems – Springer – ISBN – 3-540-21201-9
As usual, here is a retrospective of this year’s activity around BPM. The choice of the themes is mine and the order its presented is random, and do not mean any raking scheme.
Enterprise Architecture: for me it was clear that this was the year where this particular domain expertise got lost. Enterprise architecture suffers from two pains. The first is the concept division between the American School and the European School. The American divide what is business architecture and IT architecture, while the Europeans do not. Envisioning an enterprise system without seeing the whole is an. The second is enterprise architecture relies for a year in static, cumbersome, time-consuming methods to “draw” the architecture and this is not a problem of the framework used. Unfortunately, EA frameworks are not adapting to the needs of the enterprises that need to shift gears quicker. This year I did not have any answers from distinguished enterprise architects how to fix this. Unless there is someone that invents “agile EA”, looks like companies will deal with some outdated skeletons in the enterprise content systems.
Social: One of the hottest themes. But the discussion that is being taken is not the most important. I mean there is lot of hype around building a social practice, use the right tools to collaborate, the bring your own device (actually this is making a huge breach in enterprise architecture) engaging with the customers, etc. But the most important aspects are being forgotten: understand how people are engaged and how it should relate. In other words look to the social dimension of a process. This year, many books about social business were published. I put my hand on two from prominent experts and the ease it took to read the books, seems this area is being treated superficially (probably because is new and there is a need to understand the basics). I measure a domain maturity for the difficulty it is to understand (the more difficult is to jump into the concepts, the more mature it is). Hence, is critical to understand how social patterns look alike (the aesthetics). Does your company have lions or lemurs? Are they working together in a way that is aligned with the knowledge type required to play the process or are you creating variability when you need standardization or vice versa? In other words, how do you put people evolving and gaining new competences to handle the exponential complexity we are facing? How do you put people learning with the others?
It is a commonplace that the world is getting more complex and that organizations are struggling to cope with it with their operations. This is difficult, because complexity is hard to describe. The language of networks patterns can bring visibility how complexity can be handled properly. A good reflexion can be found <here> .
Intelligence. This category includes many scattered sub categories all of them important. They are: big data, mining and prediction.
Before jumping into each knowledge domain, it’s important to make a reflection about why humans are so fascinated with it. We like to predict events but we miserably fail to do it. We like to be sure we do not get wrong in our assumptions. But due changing conditions that we rely to make our wizardry it’s getting more difficult to do it. Sometimes we do not realize that the facts we use to predict changed when the event occurs. That happens because the world is a complex system and there is no linearity in how each variable is related. Thus, we are looking for new predictions models and technology that can help us with one of the major human limitations: being capable to be correct about the future.
Technology can help us, broadly speaking, to make better decisions, but being able to “be sure” is a step that probably is still too far to be a reality. Why? Because intelligent systems do not have the capacity to learn. In a previous experience with IBM’s Watson super computer when asked about what is the capital of North Korea it replied “this country does not have diplomatic relationship with the USA”. The machine was not able to find other way of search and get an answer (actually it did not realize it was wrong). In addition, because machines suffer from what is called cognitive illusion (to understand better the concept read this article how out of context, a system – it can be a human as system we are – makes the wrong decisions).
Talking about predictions, this could take us to a long discussion dedicated only to the subject, nevertheless, strictly speaking within the human dimension, it’s interesting to distinguish two types of person:
• More P or M individual in Gordon Pask’s words;
• More power of Ideas or power of Politics in Rick Brandon’s and Marty Seldman’s words, or;
• More Fox or Hedgehogs in Philip Tetlock’s words.
The former are humans who like to question, evaluate options and engage with others, like to share knowledge and evolve concepts and like to learn with failure.
The last are humans that believe in formal authority, governance, centrality, control, and their ideas cannot be questioned, are somewhat like dogmas.
Foxes, power of Ideas or P Individuals, like o spend time to construct models to understand reality, than unfortunately changes and the models are useless or return wrong answers.
Hedgehogs, politics and M individuals like to blame the errors due to bad luck. This type of human predicts what is called under “smelling felling” approach.
Coping only with this human type variability, should make us wonder why is so difficult to predict.
Talking a about big data, the challenge is real and is here, and without a question is important to get important information about organizations, operations, but 99,99% is noise. In other words, is not relevant. The challenge for big data is to process photos, video, documents where there are data chunks that can be important to understand how good you perform, or to execute sentiment analysis for example.
Scepticism is around process mining. People do not like to believe that it is possible to discover process models and analyse a process from different points of views because they tend to think that there is no data for that. Also, because we know that when exceptions occur, process participants jump off the systems and start collaborating using e-mail. Fortunately, process mining is much more powerful and quick to analyse a process and it has out of the box a huge palette of techniques even in the case where the is no event log.
Self-organization: I will dedicate in the future a post only to this matter, but for know, I sense a shift in the way that companies are embracing change projects. Somehow we were used to implement governance models, to help others to make the change, to design and implement business processes, in other words to sell professional services (from the provider point of view). One of the shifts I talked about at the BPM conference in London this year, the social factor, as I put it: “On the social factor, [...] we are facing a displacement of “assembly line” people [...] will have to adapt and start pushing their capabilities to new boundaries. This shift has also a profound implication on the type of people companies are sourcing in the labour market. As leading companies expand and operations are outsourced or transferred to low wages economies, the future workers profile will be aimed at highly skilled persons capable of embracing business dynamics”.
People will also question the existing order, like the pianist Glenn Gould, that opposed the way Bach composed. He said that Bach made some errors and some notes should be changed .
The social factor is changing the profile of the people that work at the companies. It’s time to teach these different type of people how to get most of the systems and how to design business processes, because they do not need you anymore, as a provider, as a mentor, as a manager, to do their job, them they will auto organize to deliver it internally. Not only the type of technology will change dramatically and will put people controlling directly process composition, letting IT embedding business logic and system interoperability, as also governance models as we know it, tend to disappear our will be less formal, it will define at maximum the rules of the game people must comply.
Self-organization must not be confused with anarchy, but it will a key human component as finally organizations are seen like organisms, living things that must fight for survival like any other animal. It must adapt or die. Cybernetic management will emerge.
Interested in 2011 list? Click <here>.
 References: Booklet – Bach: The Goldberg Variations.
As usual, this is my 2012 list of BPM blogs I think work reading.
- Adam Dean: use your BPM illusion. Adam is a true story teller.
- Blogging internet of things – It’s all about … internet of things.
- Bp-3: Scott Francis’s blog brings the most balanced views.
- BPM redux – Theo Priestley’s posts are like Salvador Dali’s paintings.
- BPM for real – this is a true BPM supermarket.
- Bouncing thoughts – Jaisundar’s writes about BPM.
- Column 2: For me the best analyst blog. Great insights from BPMS systems and event coverage brought by Sandy Kemsley.
- Flux Capacitor – The place to look for process mining, from Fluxicon.
- Ground-Floor BPM – E. Scott Menter deconstructs the myths of BPM and BPMS.
- Improving Enterprise BPMS – A great blog to look for frameworks by Alexander Samarin.
- Interactive Value Creation – Esko Kilpi provides amazing insights about complexity and new working patterns.
- Jim Sinur: Some intelligent thoughts about BPM future trends.
- On collaborative planning: Keith Sewson brings insights around social collaboration.
- On Web Strategy – Dion Hinchcliffe writes about social platforms and social business.
- Paul Mathiesen’s – Paul Mathiesen writes about social business.
- Product Four – Deb Lavoy writes about enterprise 2.0, social and collaboration.
- Social Enterprise – Emanuele Quintarelli writes about social software and collaboration.
- Sonnez en can d’absence – Adaptation, complexity, chaos, brought to you from Thierry de Baillon.
- Team cognition – Exploring new ways of learning.
- The Smart Work Company – Anne Marie’s writes about the future of work, business processes and social collaboration.
- Tetradian – Tom Grave´s writes about enterprise architecture.
- Welcome to the real It world: Max Pucher writes about what is going to be a reality in the next 5 years (but it was not yet invented).
If you are interested in 2011 list click here. Till next year!
On April this year I wrote a post about one of the challenges that Process Mining is not addressing properly: Big data. In the last process mining task force meeting, resulted that one the biggest concerns was about the effort of making XES a “de facto” standard in order event logs used in process mining could be system interoperable.
Despite the fact is important to assure that data can be exchanged across multiple systems and they can be interpreted the same manner (contrary what happens with bpmn diagrams), I tend to think that process mining is loosing ground regarding the near future reality:
- Technology will be like electricity or if you prefer a commodity;
- Cybernetics principles will emerge (finally) because society and technology is backing humans able to observe, sense, act, learn and adapt. Once organizations are made from humans, and despite humans belong to some kind of organizational unit, they are not anymore animals on a tree branch and are part of social networks that constitute the enterprise where everything happens.
- Technology no longer shapes the way human perform. People do it. Change and adaptation encompasses clearly to think and find new business models. To think about innovation. If companies do not have humans capable to find access, understand and process information, how can companies evolve and survive?
Again what is the challenge of process mining?
Organizations live in a world where interdependence, self-organization and emergence are factors for agility, adaptability and flexibility plunged into networks. Software-based information systems go into a service oriented architecture direction and the same goes to Infrastructures where services are become structures available in networks. inspired into empirical studies of networked systems such as Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us structurally understand or predict the behavior of these systems. Those findings are characterized by been supported on the “complex networks” concepts .
Last week I stumbled into a great find based on a conversation about creating an Adaptive Enterprise Architecture framework to support business processes that Process Sphere is sponsoring, in order to apply in real world implementations. Universidade do Minho is leading research in the field of understanding existing information systems can be characterized adopting the complex adaptive systems principles.
The approach is quite obvious (and at the same time so simple). Is about a black box (supplied by Palo Alto networks) connected to the enterprise network (the pipes where information flow, where today everything flows) and literally sucks all the information from the pipe to be analyzed and interpreted. With this approach its possible to understand Who interacts an with Whom interacts, What information is accessed and processed, What and When, interaction occurs, What are the information services requested and (in my point of view) particularly important evaluating how information systems can cope with social iteration and can keep the pace of adaptability inside of organizations (something I’m keen regarding other project about requirements engineering using process mining).
The innovation of the approach is based into:
- Analysis is carried based on complex network principles;
- Contrary of one of process mining principles, that the staring point is to select data to make the analysis in order to prevent “data collection paranoia”, this project collects and analyses ALL the available data and digs into the networks that were formed (each network have a different analysis dimension depending on the field of analysis). Layered.
On one hand the method approach to interpret data it’s arguable among the scientific community that the one here presented is inferior of others used by other researchers that develop process mining methods available in tools (researchers tend to think they are above God), thus I’m not going to involve in that kind of discussion (by the way this one comes from physics), on the other hand, what I like most is the design approach. Putting a box so suck all the data to be explored (into the company veins) what could be called inline real time process mining to deal with complexity and adaptability.
References:  Modeling Organizational Information System Architecture Using “Complex Networks” Concepts, José L.R. Sousa, Ricardo J. Machado, J.F.F. Mendes, 2012 Eighth International Conference on the Quality of Information and Communications Technology
How Knowledge Workers Get Things Done
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- Let's transform the process #bpm process analysis and transformation class http://t.co/p4MRjJANjN 1 day ago
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