2. Introduction to Data Mining

Data mining is one of the most powerful enablers of analytics. Although its roots date back to the late 1980s and early 1990s, the most impactful data mining applications have been developed after the turn of the 21st century. Many people believe that the recent popularity of analytics can largely be credited to the increasing use of data mining, which is extracting and providing much-needed insight and knowledge to decision makers at all levels of the managerial hierarchy. The term data mining was originally used to describe the process through which previously unknown patterns in data were discovered. Software vendors and consulting companies have expanded this definition to include most forms of data analysis in order to increase its reach and capability and thereby increase sales of software tools and services related to data mining. With the emergence of analytics as an overarching term for all data analyses, data mining is put back into its proper place—on the analytics continuum where the new discovery of knowledge happens.

In an article in Harvard Business Review, Thomas Davenport (2006), a well-known and respected expert in the field of analytics, argued that the latest strategic weapon for today’s businesses is analytical decision making that is based on discovery of new knowledge through data mining. He provided examples of companies such as Amazon.com, Capital One, Marriott International, and others that have used (and are still using) analytics to better understand their customers and optimize their extended supply chains in order to maximize their returns on investment while providing the best possible customer service. This level of success can happen only if the company pursues all avenues, including analytics at all three levels—descriptive, predictive, and prescriptive—to intimately understand its customers and their needs and wants, along with their vendors, business processes, and the extended supply chain.

Data mining is the process of converting data into information and to knowledge. In the context of knowledge management, data mining is the phase in which new knowledge is created. Comparatively speaking, knowledge is very distinct from data and information (see Figure 2.1). Whereas data is facts, measurements, and statistics, information is organized or processed data that is timely (i.e., inferences from the data are drawn within the time frame of applicability) and understandable (i.e., with regard to the original data). Knowledge is information that is contextual, relevant, and actionable. For example, a map that gives detailed driving directions from one location to another could be considered data. An up-to-the-minute traffic bulletin along the freeway that indicates a traffic slowdown due to construction several miles ahead could be considered information. Awareness of an alternative, back-roads route could be considered knowledge. In this case, the map is considered data because it does not contain current relevant information that affects the driving time and conditions from one location to the other. However, having the current conditions as information is useful only if you have knowledge that enables you to avoid the construction zone. The implication is that knowledge has strong experiential and reflective elements that distinguish it from information in a given context.

Having knowledge implies that it can be exercised to solve a problem, whereas having information does not carry the same connotation. An ability to act is an integral part of being knowledgeable. For example, two people in the same context with the same information may not have the same ability to use the information with the same degree of success. Hence, there is a difference in the human capability to add value. The differences in ability may be due to different experiences, different training, different perspectives, and other factors. Whereas data, information, and knowledge may all be viewed as assets of an organization, knowledge provides a higher level of meaning about data and information. Because it conveys meaning, it tends to be much more valuable yet more ephemeral.

Although the term data mining is relatively new to many people, the ideas behind it are not. Many of the techniques used in data mining have their roots in traditional statistical analysis and artificial intelligence work done since the early part of 1950s. Why, then, has data mining suddenly gained the attention of the business world? Following are some of the most common reasons:

• More intense competition at the global scale. There are more suppliers than there is demand to satisfy everybody.

• Constantly changing needs and wants of the customers. Because of the increasing number of suppliers and their offerings (e.g., higher quality, lower cost, faster service), customer demand is changing in a dramatic fashion.

• Recognition of the value of data. Businesses are now aware of the untapped value hidden in large data sources.

• Changing culture of management. Data-driven, evidence-based decision making is becoming a common practice, significantly changing the way managers work.

• Improved data capture and storage techniques. Collection and integration of data from a variety of sources into standardized data structures enable businesses to have quality data about customers, vendors, and business transactions.

• Emergence of data warehousing. Databases and other data repositories are being consolidated into a single location in the form of a data warehouse to support analytics and managerial decision making.

• Technological advancements in hardware and software. Processing and storage capabilities of computing devices are increasing exponentially.

• Cost of ownership. While capabilities are increasing, the cost of hardware and software for data storage and processing are rapidly decreasing.

• Availability of data. Living in the age of Internet brings new opportunities to analytically savvy businesses to identify and tap into very large and information-rich data sources (social networks and social media) to better understand the world they are living in.

Data is everywhere. For instance, data generated by Internet-based activities is increasing rapidly, reaching volumes that we did not even have specific names for in the very recent past. Large amounts of genomic data and related information (in the form of publications and research findings published in journal articles and other media outlets) are being generated and accumulated all over the world. Disciplines such as astronomy and nuclear physics create huge quantities of data on a regular basis. Medical and pharmaceutical researchers constantly generate and store data that can then be used in data mining applications to identify better ways to diagnose and treat illnesses and to discover new and improved drugs. On the commercial side, perhaps the most common use of data and data mining has been in the finance, retail, and health care sectors. Data mining is used to detect and reduce fraudulent activities, especially in insurance claims and credit card use; to identify customer buying patterns; to reclaim profitable customers; to identify trading rules from historical data; and to aid in increased profitability using market-basket analysis. Data mining is already widely used to better target clients, and with the widespread development of ecommerce, this can only become more imperative with time.

Data mining is a process that uses statistical, mathematical, and artificial intelligence techniques and algorithms to extract and identify useful information and subsequent knowledge (or patterns) from large sets of data. These patterns can be in the form of business rules, affinities, correlations, trends, or predictions. Fayyad et al. (1996) define data mining as “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases,” where the data is organized in records structured by categorical, ordinal, and continuous variables. The key terms in this definition have the following meanings:

• Process implies that data mining comprises many iterative steps.

• Nontrivial means that some experimentation-type search or inference is involved; that is, it is not as straightforward as a computation of predefined quantities.

• Valid means that the discovered patterns should hold true on new data with a sufficient degree of certainty.

• Novel means that the patterns were not previously known to the user in the context of the system being analyzed.

• Potentially useful means that the discovered patterns should lead to some benefit to the user or task.

• Ultimately understandable means that the pattern should make business sense that leads to users saying “This makes sense. Why didn’t I think of that?”—if not immediately, at least after some processing.

Data mining is not a new discipline but rather a new approach in the intersection of several other scientific disciplines. To some extent, data mining is a new philosophy that suggests the use of data and mathematical models to create/discover new knowledge. Data mining leverages capabilities of other disciplines, including statistics, artificial intelligence, machine learning, management science, information systems, and databases, in a systematic and synergistic way (see Figure 2.2). Using collective capabilities of these scientific disciplines, data mining aims to make progress in extracting useful information and knowledge from large data repositories. It is an emerging field that has attracted much attention in a very short time and has fueled the emergence and popularity of the analytics movement.

Another concept that data mining is often confused with is OLAP (online analytical processing). As the core enabler of the business intelligence movement, OLAP is a collection of database-querying methods for searching very large databases (or data warehouses) through the use of data cubes. Cubes are multidimensional representations of the data stored in data warehouses. With the use of cubes, OLAP helps decision makers to slice and dice organizational data for the purpose of finding answers to “What happened?” “Where did it happen?” and “When did it happen?” types of questions. As sophisticated as it may sound—and perhaps from the standpoint of efficiency, it actually is that sophisticated—OLAP is not data mining. It may be a precursor to data mining. In a sense, they complement each other in that they convert data into information and knowledge for better and faster decision making. Whereas OLAP is part of descriptive analytics, data mining is an essential part of predictive analytics.

There has been much discussion about statistics and data mining; some people suggest that data mining is a part of statistics and others propose that statistics is a part of data mining. Yet others suggest that data mining and statistics are the same thing. Even though we cannot get to the bottom of that discussion here, we can at least shed some light to it by mentioning a few critical points. Data mining and statistics have a lot in common. They both look for relationships in data. The main difference between the two is that statistics starts with a well-defined proposition and hypothesis, while data mining starts with a loosely defined discovery statement. Statistics collects a sample of data (i.e., primary data) to test the hypothesis, while data mining and analytics use existing data (i.e., often observational, secondary data) to discover novel patterns and relationships. Another difference is in the size of data that they use. Data mining looks for data sets that are as “big” as possible, while statistics looks for right size of data and, if the data is larger than what is needed or required for the statistical analysis, it uses a sample of the data. Statistics and data mining have deferent definitions of what constitutes “large data”: Whereas a few hundred to a thousand data points is large enough to a statistician, several millions to a few billion data points is considered large for data mining studies.

In summary, data mining is not a simple Internet search or routine application of OLAP, and it’s not the same as statistics. Even though it uses capabilities of these descriptive techniques, data mining is the next level in the analytics hierarchy, where interesting patterns (relationships and future trends) are discovered with the use of data and models.

• Associations find commonly co-occurring groupings of things, such as “beers and diapers” or “bread and butter” commonly purchased and observed together in a shopping cart (i.e., market-basket analysis). Another type of association pattern captures sequences of things. These sequential relationships can discover time-ordered events, such as predicting that an existing banking customer who already has a checking account will open a savings account followed by an investment account within a year.

• Predictions tell the nature of future occurrences of certain events based on what has happened in the past, such as predicting the winner of the Super Bowl or forecasting the absolute temperature on a particular day.

• Clusters identify natural groupings of things based on their known characteristics, such as assigning customers in different segments based on their demographics and past purchase behaviors.

These types of patterns have been manually extracted from data by humans for centuries, but the increasing volume of data in modern times has created a need for more automatic approaches. As data sets have grown in size and complexity, direct manual data analysis has increasingly been augmented with indirect, automatic data processing tools that use sophisticated methodologies, methods, and algorithms. The manifestation of such evolution of automated and semiautomated means of processing large data sets is now commonly referred to as data mining.

As mentioned earlier, generally speaking, data mining tasks and patterns can be classified into three main categories: prediction, association, and clustering. Based on the way in which the patterns are extracted from the historical data, the learning algorithms of data mining methods can be classified as either supervised or unsupervised. With supervised learning algorithms, the training data includes both the descriptive attributes (i.e., independent variables or decision variables) as well as the class attribute (i.e., output variable or result variable). In contrast, with unsupervised learning, the training data includes only the descriptive attributes. Figure 2.3 shows a simple taxonomy for data mining tasks, along with the learning methods and popular algorithms for each of the data mining tasks. Out of the three main categories of tasks, prediction patterns/models can be classified as the outcome of a supervised learning procedure, while association and clustering patterns/models can be classified as the outcome of unsupervised learning procedures.

Prediction is commonly used to indicate telling about the future. It differs from simple guessing by taking into account the experiences, opinions, and other relevant information in conducting the task of foretelling. A term that is commonly associated with prediction is forecasting. Even though many people use these two terms synonymously, there is a subtle difference between them. Whereas prediction is largely experience and opinion based, forecasting is data and model based. That is, in the order of increasing reliability, one might list the relevant terms as guessing, predicting, and forecasting. In data mining terminology, prediction and forecasting are used synonymously, and the term prediction is used as the common representation of the act. Depending on the nature of what is being predicted, prediction can be named more specifically as classification (where the predicted thing, such as tomorrow’s forecast, is a class label such as “rainy” or “sunny”) or regression (where the predicted thing, such as tomorrow’s temperature, is a real number, such as “65^°F”).

Classification, or supervised induction, is perhaps the most common of all data mining tasks. The objective of classification is to analyze the historical data stored in a database and automatically generate a model that can predict future behavior. This induced model consists of generalizations over the records of a training data set, which help distinguish predefined classes. The hope is that the model can then be used to predict the classes of other unclassified records and, more importantly, to accurately predict actual future events.

Common classification tools include neural networks and decision trees (from machine learning), logistic regression and discriminant analysis (from traditional statistics), and emerging tools such as rough sets, support vector machines, and genetic algorithms. Statistics-based classification techniques (e.g., logistic regression, discriminant analysis) have received their share of criticism—that they make unrealistic assumptions about the data, such as independence and normality—which limit their use in classification-type data mining projects.

Neural networks (see Chapter 5, “Data Mining Algorithms,” for more detailed coverage of this popular machine learning algorithm) involve the development of mathematical structures (somewhat resembling the biological neural networks in the human brain) that have the capability to learn from past experiences, presented in the form of well-structured data sets. They tend to be more effective when the number of variables involved is rather large and the relationships among them are complex and imprecise. Neural networks have disadvantages as well as advantages. For example, it is usually very difficult to provide good rationale for the predictions made by a neural network. Also, neural networks tend to need considerable training. Unfortunately, the time needed for training tends to increase exponentially as the volume of data increases, and, in general, neural networks cannot be trained on very large databases. These and other factors have limited the applicability of neural networks in data-rich domains.

Decision trees classify data into a finite number of classes, based on the values of the input variables. Decision trees are essentially a hierarchy of if–then statements and are thus significantly faster than neural networks. They are most appropriate for categorical and interval data. Therefore, incorporating continuous variables into a decision tree framework requires discretization—that is, converting continuous valued numerical variables to ranges and categories.

A related category of classification tools is rule induction. Unlike with a decision tree, with rule induction, the if–then statements are induced from the training data directly, and they need not be hierarchical in nature. Other, more recent techniques such as SVM, rough sets, and genetic algorithms are gradually finding their way into the arsenal of classification algorithms and are covered in more detail in Chapter 5 as part of the discussion on data mining algorithms.

Using associations, more commonly called association rules in data mining, is a popular and well-researched technique for discovering interesting relationships among variables in large databases. Thanks to automated data-gathering technologies such as bar code scanners, the use of association rules for discovering regularities among products in large-scale transactions recorded by point-of-sale systems in supermarkets has become a common knowledge-discovery task in the retail industry. In the context of the retail industry, association rule mining is often called market-basket analysis.

Two commonly used derivatives of association rule mining are link analysis and sequence mining. With link analysis, the linkage among many objects of interest is discovered automatically, such as the link between Web pages and referential relationships among groups of academic publication authors. With sequence mining, relationships are examined in terms of their order of occurrence to identify associations over time. Algorithms used in association rule mining include the popular Apriori (where frequent item sets are identified), FP-Growth, OneR, ZeroR, and Eclat. Chapter 4, “Data and Methods in Data Mining,” provides an explanation of Apriori.

Clustering partitions a collection of things (e.g., objects, events, etc., presented in a structured data set) into segments (or natural groupings) whose members share similar characteristics. Unlike in classification, in clustering, the class labels are unknown. As the selected algorithm goes through the data set, identifying the commonalities of things based on their characteristics, the clusters are established. Because the clusters are determined using a heuristic-type algorithm, and because different algorithms may end up with different sets of clusters for the same data set, before the results of clustering techniques are put to actual use, it may be necessary for an expert to interpret and potentially modify the suggested clusters. After reasonable clusters have been identified, they can be used to classify and interpret new data.

Not surprisingly, clustering techniques include optimization. The goal of clustering is to create groups so that the members within each group have maximum similarity and the members across groups have minimum similarity. The most commonly used clustering techniques include k-means (from statistics) and self-organizing maps (from machine learning), which is a unique neural network architecture developed by Kohonen (1982).

Firms often effectively use their data mining systems to perform market segmentation with cluster analysis. Cluster analysis is a means of identifying classes of items so that items in a cluster have more in common with each other than with items in other clusters. This type of analysis can be used in segmenting customers and directing appropriate marketing products to the segments at the right time in the right format at the right price. Cluster analysis is also used to identify natural groupings of events or objects so that a common set of characteristics of these groups can be identified to describe them.

Two techniques often associated with data mining are visualization and time-series forecasting. Visualization can be used in conjunction with other data mining techniques to gain a clearer understanding of underlying relationships. As the importance of visualization has increased in recent years, the term visual analytics has emerged. The idea is to combine analytics and visualization in a single environment for easier and faster knowledge creation. Visual analytics is covered in detail in Chapter 4. In time-series forecasting, the data consists of values of the same variable that is captured and stored over time, in regular intervals. This data is then used to develop forecasting models to extrapolate the future values of the same variable.

Most of the business intelligence (BI) tool vendors (e.g., IBM Cognos, Oracle Hyperion, SAP Business Objects, Microstrategy, Teradata, Microsoft) also have some level of data mining capabilities integrated into their software offerings. These BI tools are still primarily focused on descriptive analytics in the sense of multidimensional modeling and data visualization and are not considered to be direct competitors of the data mining tool vendors.

In addition to the commercial data mining tools, there are several open source and/or free data mining software tools available over the Internet. Historically, the most popular free (and open source) data mining tool is Weka, which is developed by a number of researchers from the University of Waikato in New Zealand (which can be downloaded from cs.waikato.ac.nz/ml/weka/). Weka includes a large number of algorithms for different data mining tasks and has an intuitive user interface. Another recently released and quickly popularized free (for noncommercial use) data mining tool is RapidMiner, developed by RapidMiner.com (which can be downloaded from rapidminer.com). Its graphically enhanced user interface, use of a rather large number of algorithms, and incorporation of a variety of data visualization features set it apart from the rest of the other free data mining tools. Another free and open source data mining tool with an appealing graphical user interface is KNIME (which can be downloaded from knime.org).

The main difference between commercial tools, such as Enterprise Miner, IBM SPSS Modeler, and Statistica, and free tools, such as Weka, RapidMiner, and KNIME, is often the computational efficiency. The same data mining task involving a large data set may take a lot longer to complete with the free software, and for some algorithms, it may not even complete (i.e., it may crash due to the inefficient use of computer memory). Table 2.1 lists the major data mining products and their Websites.

A suite of business intelligence capabilities that has become increasingly more popular for data mining studies is Microsoft’s SQL Server. With this tool, data and models are stored in the same relational database environment, making model management a considerably easier task. The Microsoft Enterprise Consortium serves as the worldwide source for access to Microsoft’s SQL Server 2012 software suite for academic purposes (i.e., teaching and research). The consortium has been established to enable universities around the world to access enterprise technology without having to maintain the necessary hardware and software on their own campuses. The consortium provides a wide range of business intelligence development tools (e.g., data mining, cube building, business reporting) as well as a number of large, realistic data sets from Sam’s Club, Dillard’s, and Tyson Foods. The Microsoft Enterprise Consortium is free of charge and can be used only for academic purposes. The Sam M. Walton College of Business at the University of Arkansas hosts the enterprise system and allows consortium members and their students to access these resources using a simple remote desktop connection. The details about becoming a part of the consortium along with easy-to-follow tutorials and examples can be found at http://enterprise.waltoncollege.uark.edu/mec.asp.

In May 2014, kdnuggets.com (a well-known Web portal for data mining and analytics) conducted its 15th annual software poll on the following question: “What analytics, data mining, data science software/tools have you used in the past 12 months for a real project?” This poll received huge attention from the analytics and data mining community and vendors, attracting 3,285 voters. The poll measures both how widely a data mining tool is used and how strongly the vendors advocate for their tool. Here are some of the interesting findings that came out of the poll:

• Many data miners use more than one tool to carry out analytics projects. According to the poll, the average number of tools used by a person or vendor was 3.7 in 2014 (compared to 3.0 in 2013).

• The separation between commercial and free software continues to shrink. In 2014, 71% of voters used commercial software and 78% used free software. About 22% used only commercial software (down from 29% in 2013). About 28.5% used free software only (down only slightly from 30% in 2013). 49% used both free and commercial software (up from 41% in 2013). These numbers suggest that more users are including free, open source tools to their analytics arsenals.

• About 17.5% of voters report having used Hadoop or other Big Data tools (a measurable increase from 14% in 2013). This is an indicator of the increased popularity of Big Data tools and technologies.

The poll found these to be the top 10 tools, by share of users:

1. RapidMiner, 44.2% share (compared to 39.2% in 2013)

2. R, 38.5% (compared to 37.4% in 2013)

3. Excel, 25.8% (compared to 28.0% in 2013)

4. SQL, 25.3% (not available in 2013)

5. Python, 19.5% (compared to 13.3% in 2013)

6. Weka, 17.0% (compared to 14.3% in 2013)

7. KNIME, 15.0% (compared to 5.9% in 2013)

8. Hadoop, 12.7% (compared to 9.3% in 2013)

9. SAS base, 10.9% (compared to 10.7% in 2013)

10. Microsoft SQL Server, 10.5% (compared to 7.0% in 2013)

Among the tools with at least 2% share, the highest increase in 2014 was for Alteryx, which saw an increase of 1079%, to 3.1% in 2014 from 0.3% in 2013; SAP (including BusinessObjects, Sybase, and Hana), with an increase of 377%, to 6.8% from 1.4%; BayesiaLab, with an increase of 310%, to 4.1% from 1.0%; and KNIME, with an increase of 156%, to 15.0% from 5.9%. Among the tools with at least 2% share, the largest decline in 2014 was for StatSoft Statistica (now part of Dell), down 81%, to 1.7% share in 2014, from 9.0% in 2013 (partly due to lack of campaigning for Statistica, now that it is part of Dell); Stata, down 32%, to 1.4% from 2.1%; and BM Cognos, down 24%, to 1.8% from 2.4%.

Figure 2.4 shows the results of the poll for tools that received 100 or more votes. In the chart, the name of a tool is followed by the number of votes in parentheses and the “alone vote” percentage. The alone vote percentage is the percentage of voters who used only that tool, all by itself. For example, just 0.9% of Python users used only Python, while 35.1% of RapidMiner users indicated they used that tool alone.

To reduce bias through multiple voting, in this poll, KDNuggets used email verification, which may potentially reduce the total number of votes but made the results less biased and more representative.

In a number of instances in the recent past, companies have shared their customer data with others without seeking the explicit consent of their customers. For instance, as you might recall, in 2003, JetBlue Airlines provided more than a million passenger records of its customers to Torch Concepts, a U.S. government contractor. Torch then subsequently augmented the passenger data with additional information, such as family sizes and Social Security numbers—information purchased from a data broker called Acxiom. The consolidated personal database was intended to be used for a data mining project in order to develop potential terrorist profiles. All this was done without notification or consent of passengers. When news of the activities got out, however, dozens of privacy lawsuits were filed against JetBlue, Torch, and Acxiom, and several U.S. senators called for an investigation into the incident (Wald, 2004). Similar, but not as dramatic, privacy-related news came out in the recent past about popular social networking companies, which were allegedly selling customer-specific data to other companies for personalized targeted marketing.

Another peculiar story about data mining and privacy concerns made it to the headlines in 2012. In this instance, the company did not even use any private and/or personal data. Legally speaking, there was no violation of any laws. This story, which is about Target, goes as follows. In early 2012, an infamous story broke out about Target’s practice of using predictive analytics. The story was about a teenager girl who was being sent advertising flyers and coupons by Target for the kind of things that a new mother-to-be would buy from a store like Target. The story goes like this: An angry man went into a Target outside Minneapolis, demanding to talk to a manager: “My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture, and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

As it turns out, Target figured out a teen girl was pregnant before her father did! Here is how they did it. Target assigns every customer a Guest ID number (tied to the person’s credit card, name, or email address) that becomes a placeholder to keep a history of everything the person has bought. Target augments this data with any demographic information that it has collected from the customer or bought from other information sources. By using this type of information, Target had looked at historical buying data for all the women who had signed up for Target baby registries in the past. They analyzed the data from all directions, and soon, some useful patterns emerged. For example, lotions and special vitamins were among the products with interesting purchase patterns. Lots of people buy lotion, but Target noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium, and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals that she could be getting close to her delivery date. Target was able to identify about 25 products that, when analyzed together, allowed them to assign each shopper a “pregnancy prediction” score. More importantly, Target could also estimate her due date to within a small window, so the company could send coupons timed to very specific stages of her pregnancy.

If you looked at this practice from a legality perspective, you would conclude that Target did not use any information that violates customers’ privacy; rather, it used transactional data that almost every other retail chain is collecting and storing (and perhaps analyzing) about their customers. What was disturbing in this scenario was perhaps that pregnancy was the targeted concept. People tend to believe that certain events or concepts should be off-limits or treated extremely cautiously, such as terminal disease, divorce, and bankruptcy.

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