3-4E Attracted by the success of Spotify- a group of students wants to.pdf

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3.4E Attracted by the success of Spotify, a group of students wants to build its own music streaming website called Musicmatic. Being economists, they are unaware of the specificities of databases and have therefore asked you to create an EER model. A large number of songs will be made available through their website, and the following information on each song needs to be stored: title, year, length, and genre. Also, artist information will be added, including date of birth, name and a URL to a website (e.g., Wikipedia page) with additional information on the artist. You can assume an artist is uniquely identified by his/her name, and that a song always belongs to exactly one artist. The Musicmatic students also point out that songs having the same title are possible, and only the combination of song and artist can be assumed to be unique. The database will also have to store information on the people using Musicmatic. It was decided to only discriminate between two types of users: the regular users who will be able to buy music, and the business users who will deliver the content (upload the music). The following information is recorded on each user: (unique) ID, name, and address. Business users will also have a VAT number. The students want to offer a flexible service, and decided business users can only upload individual songs. These songs are classified either as singles or hits, and regular users can directly buy the singles. Otherwise, people can compose an album consisting of multiple hits (no singles). The position of each hit in the album is stored as a track number in the database. Note that the album of regular users can be turned into a suggestion to other regular users with similar purchasing behavior. Finally, a user can be a regular user on some occasions (e.g., when downloading a single or album), and a business user at other times (e.g., when uploading self-made songs to Musicmatic). Make an EER model and UML class diagram to model the data requirements. Comment on the limitations of both models..

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Create a diagram for the following scenario The music streaming service features different types of users based on their access type: regular users, superusers, and system administrators. Artists may create songs that users can listen to, some songs created by multiple artists. Each artist has a unique Artist ID, name, and genre. Songs represent individual music tracks available on the platform that can be added to user playlists; each song has Song ID, title, duration, and release date. Playlists are collections of songs selected by users, each playlist have a unique Playlist ID, name, and creation date. Users might create empty playlists. System records updated date every time user updates playlist. Users can access multiple on-demand reports, including a monthly usage report, favorite songs report, songs genre report, and others. Hint: Do not assume anything, do not add unnecessary details, and do not overthink.

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Find a publicly-traded company (Microsoft) using a financial information website. For The Following Company : Microsoft Watch the Industry Averages and Financial Ratios video and use the industry classification from the financial services website to locate the company's SIC code on the U.S. Department of Labor's website. Find the industry ratios for the company using the Dun & Bradstreet® Key Business Ratios link in the Week 2 Electronic Reserve Readings. If your company's SIC code does not appear in the dropdown menu, choose another company. Calculate the following ratios for the company using the two most recent annual financial statements found on the financial information website you used earlier. Be careful not to use quarterly information, and include ratios for both years. · Current Ratio · Quick Ratio · Collection Period · Return on Equity (~Net Worth) Observe the year to year ( 2 years ) trend for each ratio and what it tells you about the organization's financial health. Compare the ratios for the company you selected with the appropriate industry ratios shown on the Dun & Bradstreet® report. Write a 1,050-1,400 word response about how the company you selected performed year to year and versus the industry based on what the ratios indicate. Show your calculations for the ratios and attach copies of the financial statements used as an exhibit. Case4 Read the case on page 183 entitled The Digital Music Distribution Revolution. The Digital Music DistributionRevolution1 Fraunhofer and MP3 In 1991, Fraunhofer IIS of Germany developed an algorithm that would set in motion a revolution in how music was distributed, stored, and consumed. The algorithm ( commonly referred to as a codec) allowed compression of digital audio to approx-imately one- tenth of its original size with minimal compromise in audible quality. The format also enabled song information such as the song title and artist to be embedded within the file. This format for compressed audio files was later dubbed MPEG- 1 layer 3— a. k. a. MP3. By 1995, software programs were available that enabled consumers to convert tracks from compact discs to MP3 files. This tech-nology transformed how music could be manipulated— a song was now a file that could be kept on a hard drive, and the file was small enough to be shared over the Internet. The MP3 format became wildly popular by users sharing their music online, and software companies began releasing many variants of MP3 encoders ( utilities that compress files into MP3s) and decoders ( utilities that play back MP3s). Hardware manufacturers decided to capitalize on this new trend and several hardware MP3 players began appearing on the market. With the growing popularity of the file format, Fraunhofer was faced with a dilemma— should it enforce its patent on the use of the MP3 algorithm and attempt to collect royalties for its use, or should it allow users and software/ hardware man-ufacturers to make free use of the alg ...

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UPDATED VERSION (2011): http://www.slideshare.net/plamere/music-recommendation-and-discovery As the world of online music grows, music 2.0 recommendation systems become an increasingly important way for music listeners to discover new music. Commercial recommenders such as Last.fm and Pandora have enjoyed commercial and critical success. But how well do these systems really work? How good are the recommendations? How far into The Long Tail do these recommenders reach? In this tutorial we look at the current stateof theart in music recommendation. We examine current commercial and research systems, focusing on the advantages and the disadvantages of the various recommendation strategies. We look at some of the challenges in building music recommenders and we explore some of the ways that MIR techniques can be used to improve future recommenders.

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Consider the following requirements for a library database. Create the Entity-Relationship model ERD for the following application. Coyote Records has decided to store information about musicians who perform on the albums (as well as other company data) in a database. The company has wisely chosen to hire you as a database designer (at your usual consulting fee of $2599 day). Each musician that records at Coyotes has an SSN, a name, an address, and a phone number. Poorly paid musicians often share the same address, and no address has more than one phone. Each instrument used in songs recorded at Coyotes has a unique identification number, a name e.g, guitar, synthesizer, flute) and a musical key (e.g, C, B-flat, E-flat). Each album recorded on the Coyotes label has a unique identification number, a title, a copyright date, a format (e.g, CD or MC), and a album identifier. Each song recorded at Coyotes has a title and an author. Each Musician may play several instruments, and a given instrument may be played by several musicians. Each album has a number of songs on it, but no song may appear on more than one album, Each song is performed by one or more musicians, and a musician may perform several songs. Each album has exactly one musician who acts as its producer. A musician may produce several albums, of course. Design a conceptual schema for Coyotes and draw an ER diagram for our schema. The preceding information describes the situation that the Coyotes database must model. Be sure to indicate all key and cardinality constraints and assumption you make. Identify any constraints you are unable to capture in the ER diagram and briefly explain why you could not express them. Can you draw out the Entity-Relationship Model ERD and also answer the questions. Please and thank you!.

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The process of finding music can be highly subjective and dependent on personal preferences. What may be considered the “best” process for one individual may not be the same for another. However, there are several common approaches that people use to discover and find new music. Here are some of the commonly used methods: Streaming Platforms: With the rise of music streaming platforms like Spotify, Apple Music, and YouTube Music, many people find music through curated playlists, personalized recommendations, and algorithm-based suggestions. These platforms analyze users’ listening habits and preferences to provide tailored music suggestions, making it easier to discover new artists and genres. Online Music Blogs and Magazines: Music blogs and online magazines often feature reviews, interviews, and curated playlists that highlight emerging artists and new releases. Many music enthusiasts follow these platforms to stay updated on the latest trends and recommendations in different genres. Social Media: Social media platforms, such as Instagram, Twitter, and TikTok, have become popular channels for discovering music. Artists, influencers, and music enthusiasts often share and recommend songs or artists through their profiles, posts, or dedicated music-related accounts. Hashtags and trending challenges can also contribute to music discovery. Radio and Podcasts: Traditional radio stations and online radio platforms still play a significant role in music discovery. DJs, music programs, and specialty shows feature diverse genres and introduce listeners to new music. Similarly, podcasts focused on music provide platforms for discussions, interviews, and recommendations, offering an avenue to explore different artists and genres. Recommendations from Friends and Peers: Personal recommendations from friends, family, or colleagues can be a valuable source for discovering new music. Sharing playlists, exchanging music recommendations, or attending live music events together can expose you to different artists and expand your musical horizons. Music Festivals and Concerts: Attending music festivals and concerts allows you to experience live performances and discover artists you may not have encountered otherwise. Many festivals feature diverse lineups and stages dedicated to different genres, providing opportunities to explore and find new music.

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