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Sound Index: Charts For the People, By the People

Mining the wisdom of the online crowds generates music business intelligence, identifying what's hot and what's not.
  1. Introduction
  2. Solution
  3. Challenges
  4. Related Work
  5. Pilot
  6. Conclusion
  7. Acknowledgments
  8. References
  9. Authors
  10. Footnotes
  11. Figures
complex data illustration

How music charts are created has remained relatively the same for the past 50 years despite dramatic shifts in the industry’s underlying business, technological, market, and cultural assumptions. The charts, which are generated and published periodically, are based largely on retail sales and radio-listener statistics. However, one of the most significant demographics for the industry—the teen market—has notably altered its new-music-consumption behavior due to the recent availability of online content and digital downloads. This phenomenon is recognized by chart creators eager to incorporate these observations into corporate marketing strategies in order to stay relevant to the younger generation and generate sales.

The Sound Index system demonstrates a new way to measure popularity in the world of music by incorporating the Web, online communities, and social networks. It enables the capture of what’s hot and what’s not on the Web while tracking the popularity of emerging records and artists in real time. It allows the music industry to keep tabs on the demographic it considers most important and for the public to quickly learn about new music.

Music charts are useful decision-support tools that influence the visibility and success of artists, as well as help calculate their financial rewards. Popularity drives radio and television programming decisions concerning the music to be covered, the resources to be allocated, and the premiums ultimately paid to artists and their representatives. These charts are critical to the continued success of musicians, as well as music-industry professionals.

Since the late 1990s, the Web has emerged as the most popular medium for young people worldwide. Hundreds of millions of users have moved to the Web to listen to music, explore new music, and purchase individual songs, ringtones, records, and albums. In fact, 48% of teens in the U.S. did not buy a single CD in 2007, up from 38% in 2006.12 Thus, traditional music charts are losing their relevance and appeal to their key demographics.15,16 Recognizing this long-term business and cultural trend, music-chart-generating organizations have begun to incorporate digital streams, but these streams still make up only a small proportion of the data reflected in the charts. In summer 2009, Apple’s iTunes, which sells digital singles downloads, was the largest music retailer in the U.S. in terms of revenue.

In the U.S., Billboard ( has published the Billboard Hot 100 music charts every week since 1958 ( In the U.K, the British Broadcasting Corporation (BBC) has published its Top of the Pops ( music charts since 1964. Similar charts are published in many other countries. As an examplar, and without loss of generality, we detail here how Billboard generates its charts, highlighting the reasons for their diminishing relevance.

Traditional charts. Billboard captures data from multiple sources to produce a composite ranking of individual songs, aka singles. Its two primary sources are Nielsen Soundscan ( and Broadcast Data Systems ( Soundscan tracks sales data in real time across the U.S. and Canada. Because not all retail stores have Soundscan-enabled cash registers, the data retrieved from these systems represents only a limited set of total sales. However, even this limited set is an improvement over the previous mechanism used by Billboard—making thousands of individual telephone calls to stores across the U.S. to ask about sales.

Broadcast Data Systems collects Billboard radio-listener statistics gathered from companies contracted by Billboard to contribute to the chart of radio airplay. Thus, not all radio airplays are captured. Once the data is captured from Soundscan and Broadcast Data Systems, it is weighted by Arbitron statistics ( and compiled by asking a random sample of the key demographic to maintain a written diary describing each radio program listened to between the hours of 6 A.M. and midnight over a period of a few months as set by Arbitron. Each diary is returned to Arbitron by postal mail; Arbitron publishes a complete set of its statistics four times per year.

In the past few years, Billboard has moved to incorporate data from digital downloads and the like, but it still constitutes only a small percentage (about 5%) of the chart’s total points.10

Concerns. The music industry’s desire to promote and sell new music and remove long-running singles from charts has led to the fact that the older singles that consumers are still interested in are completely ignored in the charts. Music charts also lack a clear way to handle the rerelease of singles and gauge interest in music that gains popularity over a long period through word of mouth. Another issue with the historic chart-generation process is that there is no measure for the leadup to the release of albums or singles. Though consumers may discuss an upcoming album release for days, the charts do not reflect this conversation. As a result, the all-time Billboard record for single-week upward movement has been broken five times since 2006.

Meanwhile, the possibility of a new payola scandal continues to haunt radio stations and record-company executives. This illegal marketing phenomenon involves record labels paying radio stations and/or disc jockeys broadcasting, and more recently streaming, records as part of a normal day’s broadcast. U.S. federal law made the practice illegal in 1934, yet as of summer 2009, major record labels, including Clearchannel, CBS Radio, EMI, Sony BMG, Universal Music, and Warner Music, have come under federal investigation and in some cases had to pay tens of millions of dollars in fines and settlements. As radio airplay is a major component of the music charts and perceived popularity, these investigations in turn raise concerns about the validity of the traditional music charts themselves.

In order to address these issues and incorporate today’s increasingly popular platform for music consumption, the Web, the music-charts industry must keep evolving or be left behind.

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The Sound Index system catalogs the hottest artists and tracks being talked about on the Web. Incorporating “listens,” plays, downloads, sales, and comments from a multitude of online communities and social networks, it provides a current view of popular music content online; the associated filtering enables customized views of the data to learn about, say, new tracks in a particular genre of interest.

The system can be divided into four distinct parts (see Figure 1), leveraging technology called MONitoring Global Online Opinions via Semantic Extraction, or MONGOOSE ( The first, ingestion, is the act of gathering relevant unstructured and structured content from various Web sites (such as Bebo, Google Groups, iTunes, LastFM, MySpace, and You-Tube). These sources were chosen because the BBC’s review team of music-domain experts identified them as relevant and important to identifying the tastes of its target demographic—teens. The system analyzes and transforms the data into a standard schema. The now-structured content is then stored in the system’s database. Finally, the system generates music charts by applying relevant ordering schemes.

Sound Index relies on broken-English-text analytics technology, techniques for integrating information from different modalities, and ranking technologies.

Ingestion. In an ideal world, social networking data, comments, and click streams would all have a common format that sites publish, facilitating easy download and integration of information. However, most sites lack functional application programming interfaces (APIs). As a result, screen scrapinga is the rule for data ingestion,2 problematic because screen scrapers are susceptible to (even fairly minor) changes in Web sites. Unfortunately, these changes are common, as sites strive to stay fashionable in an ever-changing cultural and business environment.

Screen scrapers also require a fair amount of monitoring and maintenance. They need to log into sites and download necessary content (such as comments and view counts), transforming it into a simple format, normally just a collection of running text comments broken out (with markup removed) for further processing.

Some sites provide really simple syndication-typeb feeds that are especially useful for ingesting aggregated data (such as total listens for a particular song). Sound Index uses a combination of screen scrapers, RSS feeds, and APIs to ingest content based on the quality and reliability of each ingestion method for a given site.

Providing a reliable stream of data, even from sites that are flaky and untrustworthy, is critical to Sound Index success. As such we have developed a suite of tools and techniques to deal with common error conditions and quickly identify exotic ones and bring them to the operator’s attention. In addition to the sanity-checking of values, the system monitors a number of bulk statistics on the streams themselves at each step in the processing. This monitoring allows the system to detect when, say, the quantity of documents entering the database from MySpace is, say, half of what it was yesterday. The system then spot-checks the crawler statistics; if it sees the number of documents fetched per hour has decreased, some kind of format change is likely preventing the low-level parsers from correctly splitting the comments out of the discussion pages. While these bulk statistics don’t tell the operator or Sound Index itself why something is not working, they are quite effective at helping reveal when something is not working.

Sound Index automates simple corrective actions, including killing and restarting fetchers and flushing domain name system cachesc to correctly identify changes in, say, the targeted servers being crawled. Developing and automating these solutions is critical, as they reduce the need for early-morning service calls to system administrators. Sound Index uses Nagiosd to monitor all aspects of the system’s performance, raising flags over problems (such as no data in the ingest feed and database-connection errors). Alba et al.2 detailed additional challenges affecting Sound Index data access.

Processing. All acquired data must be “cleaned” before it undergoes processing and analysis. For example, the cleaning of structured data generally consists of a few sanity checks. For numeric data (such as total video views), which is expected to constantly increase, the system checks whether fewer total mentions were made today compared to yesterday. If they were, the implication is a negative number of views and something clearly in error.

Sound Index might report that there were zero views during this period rather than a clearly broken number for upstream processing, a scenario that is surprisingly frequent in the music domain. Also, some sources perform corrections that result in big jumps in structured numbers. As Sound Index reports data every six hours (some source numbers are updated every week), the system’s developers incorporated techniques for smoothing these numbers.

A major challenge in developing the system was figuring out how to eliminate “spam” from comment streams. Popular artists draw many visitors, a fact advertisers are quick to capitalize on. Up to 50% of a popular artist’s comments are what could be considered spam (ranging from the blatant “Check out my page <URL>” to the relatively subtle “If you like this artist you will love <URL>” to the simply off topic “I like ducks!”). As they are not music-related expressions, Sound Index needs to be able to remove them from the tally; otherwise they could easily dominate (and distort) the results.

The Sound-Index topic-detection methodology accounts for whether a post is on- or off-topic, with the latter consisting of spam or nonsense posts. Employing a combination of template spotting for extremely common spam phrases and a domain dictionary, it identifies the presence or absence of music-related terminology. This approach provides reasonable spam identification, down to where it has virtually no effect on relative counts. For on-topic posts, Sound Index extracts the relevant noun phrases, as well as the associated sentiment.

The issue of how to identify and remove spam is even more challenging due to unstructured data. Especially in the music domain, slang and nontypical spellings and linguistic constructs appear with some frequency. A good example is the comment “U R 50 Bad.” Parsing it is a complex, multi-step process. First, common variants must be rewritten into their more common English equivalents; for example, numbers as substitutions for letters must be reversed and texting abbreviations expanded. This technique results in “You are so bad” as the comment. The next step employs a feed of common slang expressions from sources like Urban Dictionary ( to rewrite slang. This gets the system to “You are very good.”

Sound Index must also identify ambiguous references. To do so it looks at all possible artists for “You.” If it appears on a fan page for, say, Amy Winehouse, the system would conclude that she is the artist most likely being mentioned. The final parsed comment becomes “Amy Winehouse is very good,” a specific mention of an artist with a positive sentiment.

The system then examines the demographic data for the poster (if available), perhaps determining that the poster is a 17-year-old female in the U.K. This data is tallied as a single mention, positive, for Amy Winehouse, by a user with said demographics. Each such data point serves as a dimension for aggregation in a subsequent step.

Resolving entity ambiguity is a major challenge in chart creation. Many song titles (such as those beginning with “The”) are difficult to spot without undergoing at least shallow parsing, a task complicated by the nontypical grammatical structures often seen in the music domain. Sound Index uses a combination of context clues, domain knowledge, and poster/venue history to track “activation” of concept nodes in a domain ontology, using these activation levels to resolve the ambiguities to the greatest extent possible. This is an area of continuing research, as current implementations are simple and error-prone with more difficult resolutions, especially in cases where a band is implied by a band member with an interesting nickname (such as “The Edge” implies “U2”).

Ultimately, Sound Index converts each data element into a row of demographic data about the poster, as well as the unique ID of any track, album, or artist mentioned, along with a notion of whether the comment is positive or negative.

Data fusion for user interface generation. All data that is cleaned and assembled (into a DB2 database) must still be coalesced to create a chart, a process that is difficult in practice, as well as in theory. How does one combine mentions of an artist on a discussion board with listens from an online radio service and views of a parody of the artist’s recent video? The various methods for creating such combinations can all be viewed as a kind of “voting” of the results of different modalities and are thus amenable to examination via voting theory. To do so, the system must first enumerate the desiderata of the data-combination system. In discussion with subject-matter experts we developed several criteria for combining music-popularity data:

  • Artists or tracks with broad support across the sources should do well in the ranking, reflecting “the wisdom of the crowds”;
  • Artists high on one source for a day and not on other sources should not be allowed to dominate the chart. This is a response to the common phenomenon whereby a group organizes a “flash mob” to post on the same day, usually in support of a new album to drive the band up the charts of a particular site. This anti-flooding criterion involves gaming resistance, enabling the system to handle users trying to influence or skew the charts in a particular direction;
  • All sources must contribute to the final chart with no single source allowed to dominate. Thus, the disparity between counts (particularly due to differences in population size) of, say, iTunes sales and YouTube views must be reconciled; and
  • The final results must be amenable to subsetting or customizable user-driven filtering; therefore, subcharts highlighting specific genera or demographics must be constructable, making it possible to produce personalized music charts.

Voting theory provides two broad classes of ways to combine these results. First is to tally the votes, perhaps through weighting; the artist or track with the most votes (plurality) is at the top of the charts. Naively counting votes is problematic, as various sources provide very different numbers; for example, sales numbers are usually much lower than views. And determining the relative importance of various modalities (such as purchases, listens, views, and posts) is subjective. Approaches like normalizing sources so their top selection is number one and weighting and combining might be the best that can be done through this approach. As long as the weights are constantly considered for changes in source popularity and the “pulsed” nature of errors in some sources is acceptable, the normalization approach reflects the important advantage of being fairly transparent. As any chart is subject to scrutiny, transparency may thus be worth the high manual cost of tracking and tuning weights.

Second is merging ranked lists, whereby each source creates a ranked list of its top-n choices. These lists are then combined without consideration of the “votes” assigned to them. For example, in Borda Counts,4 each #1 vote is worth n points, #2 is worth n minus 1 points, and so on. However, it suffers when n is very large and the number of voters is small, the reverse of typical elections but historically the case for music charts. In this approach, as n gets larger, the difference in effect between n and n minus 1 becomes relatively small. For this list, we found that the Nauru voting method3 (first place gets 1 point, second place 1/2 point, third place 1/3 point, and so on) is better at highlighting top picks. However, it is somewhat aggressive in that items ranking high on one list might also tend to dominate the overall chart. We thus introduced a variant, p, to give the system more control over this potentially skewed result. The score of an artist or track at position n thus becomes


As p varies up, that is, the system reviews entries lower on the list (such as songs at position 499 and 789) and the need for broad support becomes more pronounced. Empirical evidence suggests p ~ 2.5 is a good place to start.

These methods for combining data from multiple, music-related sources can be applied to full sets of data; alternatively, the initial data can be subsetted (such as to create a list of only, say, rap and hip-hop tracks) then “voted” on to create custom lists.

To evaluate this approach to combining list data, we applied, on the basis of the criteria set by the subject-matter experts, two social welfare functions:e precision optimal aggregation1 and Spearman Footrule distance.5 The former measures the number of artists from each source’s top-n list that made it to the overall top-n list; the latter emphasizes the preservation of an artist’s position in the ranking. We compared the performance of eight different methods, with performance defined as the efficacy of a given method in maximizing the two SWFs. For a detailed study of the comparison, see A. Alba. et al.3

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Sound Index is the first industrial-strength implementation of the complex idea of combining “dirty” multi-modal data, (see Figure 2), using unstructured information management architecture (UIMA)f, 6 and data mining7 to solve a targeted business problem. Here, we focus on two related research challenges:

Noise effects vs. freshness. Tension between the desire for frequent updates reflects the cutting edge of what is hot and the desire to minimize the influence of noise in the charts due to short-term spikes. Sound Index weighs effects (such as weekends, nights, and holidays) against events (such as new album releases, celebrity gossip coverage, and award shows). The system must ultimately compromise between being too sensitive and not reactive enough; optimizing this balance is an area for future research. For now, Sound Index employs a 24-hour window (four-to-six-hour cycle periods) to smooth out some of the effects mentioned earlier. The development team is also exploring other approaches (such as multi-month decays). Ultimately, the system needs a ranking scheme that is at least somewhat resistant to “noise” while still being able to capture freshness so, for example, it is able to identify a rise in interest in diverse sources and ignore sudden spikes in a single source.

User interface. Still unclear is the optimal way to present what is essentially an online analytical processingg cube to end users over the Web for mining business intelligence, especially when the target audience is teens. Exploring the right way to present trending and selection is key to allowing consumers of Sound Index to get the most from the system, but doing so in a way that is obvious and intuitive is a challenge. Sound Index does offer a limited set of dimensionality tools around demographics and genres, allowing users to see charts reflecting the interests of, say, “40-something female electronica fans in the U.S.”14

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Related Work

A wealth of research focuses on business intelligence mining, showcasing the value of traditional information integration and aggregation techniques,17 whereby systems compare and contrast items with identical modalities (such as sales numbers from multiple sources). Sound Index demonstrates how to integrate information from multiple different modalities (such as comments, passive listens, sales, hits on Web sites, creation of new Web sites, and views on television), a solution required in many domains, including medical-patient preferences, drugs for certain medical conditions, cars, wine, financial products like stocks and bonds, consumer goods, cameras, computers, and books.

Nielsen’s BuzzMetrics ( aims for a similar goal, at least at the abstract level. Its technology monitors and analyzes consumer-generated media (such as blogs, message boards, forums, Usenet newsgroups, discussions involving email portals like Yahoo!, AOL, and MSN, opinion and review sites, and feedback and complaint sites), then analyzes, customizes, and presents the data to marketers and business-intelligence professionals, depending on client requirements. However, as of summer 2009, no publicly available technical information is available on BuzzMetrics. We speculate that its technology relies on natural-language and sentiment processing, whereas Sound Index relies on broken-English-text analytics technology, techniques for integrating information from different modalities, and ranking technologies.

Alexa Internet ( is another technology that crawls Web sites to produce a ranked list of sites based on traffic statistics and incoming links. It aims to generate an ordered list of the sites with the greatest volume of (incoming) traffic normally filtered by geography or other criteria, an approach that differs from the one used in Sound Index to combine data from multiple modalities into a balanced ordered list.

The effort over the past decade to address these challenges8,9 represents approaches to extracting and disambiguating entities within unstructured text. Sound Index faces similar challenges, with disambiguation being required at the artist, band, track, and album levels.

Determining the entity being referred to in a particular text is akin to a classification problem, whereby content (“comment” in our case) must be assigned to a specific bucket, or category (artist, band, and/or track). Ellen Riloff13 highlighted domain-cognizant techniques for text classification; reflecting the need to focus on local linguistic context for classification and retrieval.

In terms of engineering, the world of mashups mirrors the music data requirements of Sound Index—a robust, reliable, repeatable means of gathering data from multiple, diverse online sources. ScrAPIs (Screen-scraper + API) were proposed by John Musser in 2006 as a means of mitigating the problem of unreliable or unavailable APIs from multiple content providers,11 though they, too, suffer from the issues facing traditional screen-scrapers (such as breaking down when site changes are made).

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The BBC ran the Sound Index pilot from March to August 2008. Its measures for success included feedback from its editorial team, Web-use statistics, and general feedback from the online community. Despite a complete lack of marketing and promotion budget and effort, Sound Index went from a standing start as public beta in April 2008 to attract 43,469 visits from 37,900 unique users in June 2008 when it attracted 140,383 page views at an average of 3.67 per user, each spending an average of three minutes and 40 seconds on the site, or 53 seconds per page. In August 2008, it attracted more than 772,000 Webpage references.

The Sound Index team monitored the online feedback by setting up Google Alerts on all possible permutations of the project name, manually evaluating each link. There was a lot of positive comment from the Web and from the traditional business and technology press. It was named “Web 2.0 technology of the week” by the U.K. Observer ( for several consecutive weeks (during April to August 2008), as well as “the hottest thing in music” (in March 2008) by the U.K.’s Guardian Music Monthly ( It also generated much debate in European music circles about what constitutes music popularity and what the results mean. The pilot closed August 2008, with the BBC planning for its future.

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Called the “first definitive music chart for the Internet age,”14 Sound Index is a novel demonstration of research into processing, analyzing, collating, ranking, and presenting large quantities of unstructured and structured multimodal information in response to a change in the behavior of key demographic groups and a pressing industry need to innovate or risk being irrelevant. It is a model for demonstrating a new approach to service and product delivery, integrating (in real time) multiple, relevant online information with one’s own data to drive new and significant value for, reinvigorate connection to, and strengthen brand affinity to one’s customer base.

Here, we’ve described the system’s technical underpinnings, highlighted some of the technical challenges already addressed, and showcased the engineering and research themes that require further investigation. The underlying concepts and processes are also applicable to myriad other fields that depend on the capture of Internet buzz. We hope it inspires future software products and research projects to harness the wisdom of the crowds.

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We would like to thank the BBC, specifically Geoff Goodwin, Head of BBC Switch, for its vision, support, and encouragement, as well as Alfredo Alba (IBM Almaden Research Center), Jan Pieper (IBM Almaden Research Center), Anna Liu (IBM Almaden Research Center), Bill J. Scott (formerly IBM Global Business Services), Aidan Toase (IBM Global Business Services), and IBM’s partners at NovaRising, who helped make the Sound Index system a reality.

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F1 Figure 1. Sound Index data flow.

F2 Figure 2. Screenshot of the Sound index interface from the BBc Sound index Web site (may 7, 2008).

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    1. Adali, S., Hill, B., and Magdon-Ismail, M. The impact of ranker quality on rank-aggregation algorithms: Information vs. robustness. In Proceedings of the 22nd International Conference on Data Engineering Workshops (Atlanta, GA, Apr. 3–7). IEEE Computer society, Washington D.C., 2006, 37.

    2. Alba, A., Bhagwan, V., and Grandison, T. Accessing the deep Web: When good ideas go bad. In Proceedings of the ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA) (Nashville, TN, Oct. 25–29). ACM Press, New York, 2008, 815–818.

    3. Alba, A., Bhagwan, V., Grace, J., Gruhl, D., Haas, K., Nagarajan, M., Pieper, J., Robson, C., and Sahoo, N. Applications of voting theory to information mashups. In Proceedings of the Second IEEE International Conference on Semantic Computing. (Santa Clara, CA, Aug. 4–7). IEEE Press, 2008, 10–17.

    4. de Borda, J.-C. Memoire sur les elections au Scrutin. Histoire de l'Académie Royale des Sciences 1781;

    5. Diaconis, P. and Graham, R Spearman's footrule as a measure of disarray. Journal of the Royal Statistics Society, Series B (Methodological) 39, 2 (1977), 262–268.

    6. Ferrucci, D. and Lally, A. UIMA: An architectural approach to unstructured information processing in the corporate research environment. Journal of Natural Language Engineering 10, 3–4 (2004), 327–348.

    7. Han, J. and Kambert, M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Inc., San Francisco, 2001.

    8. Hassell, J., Aleman-meza, B., and Arpinar, I.B. Ontology-driven automatic entity disambiguation in unstructured text. In Proceedings of the International Semantic Web Conference LNCS 4273 (Athens, GA, Nov. 5–9). Springer, 2006, 44–57.

    9. Lloyd, L., Bhagwan, V., Gruhl, D., and Tomkins, A. Disambiguation of References to Individuals. IBM Research Report RJ10364 (A0510–011). san Jose, CA, Oct. 28, 2005;$File/rj10364.pdf.

    10. Mayfield, G. Billboard Hot 100 to include digital streams. (July 31, 2007);

    11. Musser, J. scrAPIs. (Mar. 21, 2006).

    12. Quinn, M. and Chang, A. More teens dissing discs in favor of online tunes. Los Angeles Times (Feb. 27, 2008);,1,2028285.story.

    13. Riloff, E. Little words can make a big difference for text classification. In Proceedings of the 18th Annual ACM SIGIR Conference on Research and Development in Information Retrieval (seattle, WA, July 9–13). ACM Press, NY, 1995, 130–136.

    14. Salmon, C. Click to download. U.K. Guardian (Apr. 18, 2008);,2274132,00.html.

    15. Styvén, M. Exploring the Online Music Market: Consumer Characteristics and Value Perceptions. Ph.D. Thesis. Department of Business Administration and Social Sciences, Luleå University of Technology, Luleå, Sweden, 2007;

    16. Walsh, G., Mitchell, V.-W., Frenzel, T., and Wiedmann, K.-P. Internet-induced changes in consumer music procurement behavior: A German perspective. Journal of Marketing Intelligence & Planning 21, 5 (2003), 305–317.

    17. Zhu, H., Siegel, M.D., and Madnick, S.E. Information aggregation: A value-added e-service. In Proceedings of the International Conference on Technology, Policy, and Innovation: Critical Infrastructures (The Hague, The Netherlands, June 26–29, 2001).

    a. Screen scraping extracts data from machine-and display-friendly code.

    b. RSS is a family of Web-feed formats used to publish frequently updated works (such as blog entries, news headlines, audio, and video) in a standard format.

    c. DNS is the hierarchical naming system for Internet resources; its caches help route, resolve, and link domains to IP addresses.

    d. Nagios ( is open source network-monitoring software.

    e. SWFs map allocations of goods and rights among people to real numbers, enabling the modeling of subjectiveness and the capture of business goals in a semiheuristic way.

    f. UIMA is a component software architecture that helps develop, discover, compose, and deploy multimodal analytics for unstructured information.

    g. OLAP is an approach to answering multidimensional analytical queries.


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