04500nam 2200661 450 991081576690332120230912132048.01-119-11925-11-119-11618-X1-119-11926-X(CKB)3710000000366202(EBL)1964138(SSID)ssj0001548105(PQKBManifestationID)16145743(PQKBTitleCode)TC0001548105(PQKBWorkID)14797686(PQKB)11151289(Au-PeEL)EBL1964138(CaPaEBR)ebr11027516(CaONFJC)MIL770191(OCoLC)905919672(CaSebORM)9781848217553(MiAaPQ)EBC1964138(MiAaPQ)EBC4041046(PPN)189412003(EXLCZ)99371000000036620220150312h20152015 uy 0engur|n|---|||||txtccrAdvances in information systems setVolume 1From big data to smart data /Fernando Iafrate1st editionLondon, England ;Hoboken, New Jersey :iSTE :Wiley,2015.©20151 online resource (89 p.)Information Systems Web and Pervasive Computing SeriesDescription based upon print version of record.1-84821-755-2 Includes bibliographical references and index.Cover; Title Page; Copyright; Contents; Preface; List of Figures and Tables; Introduction; I.1. Objectives; I.2. Observation; I.2.1. Before 2000 (largely speaking, before e-commerce); I.2.2. Between 2000 and 2010 (the boom of e-commerce, then the advent of social networks); I.2.3. Since 2010 (mobility and real-time become keywords); I.2.4. And then ... (connected objects...); I.3. In sum; 1: What is Big Data?; 1.1. The four "V"s characterizing Big Data; 1.1.1. V for "Volume"; 1.1.2. V for "Variety"; 1.1.3. V for "Velocity"; 1.1.4. V for "Value", associated with Smart Data1.1.4.1. What value can be taken from Big Data?1.2. The technology that supports Big Data; 2: What is Smart Data?; 2.1. How can we define it?; 2.1.1. More formal integration into business processes; 2.1.2. A stronger relationship with transactionsolutions; 2.1.3. The mobility and the temporality of information; 2.1.3.1. The automation of analysis; 2.2. The structural dimension; 2.2.1. The objectives of a BICC; 2.3. The closed loop between Big Data and Smart Data; 3: Zero Latency Organization; 3.1. From Big Data to Smart Data for a zero latency organization; 3.2. Three types of latency3.2.1. Latency linked to data3.2.2. Latency linked to analytical processes; 3.2.3. Latency linked to decision-making processes; 3.2.4. Action latency; 4: Summary by Example; 4.1. Example 1: date/product/price recommendation; 4.1.1. Steps "1" and "2"; 4.1.2. Steps "3" and "4": enter the world of "SmartData"; 4.1.3. Step "5": the presentation phase; 4.1.4. Step "6": the "Holy Grail" (the purchase); 4.1.5. Step "7": Smart Data; 4.2. Example 2: yield/revenue management (rate controls); 4.2.1. How it works: an explanation based on the Tetrisprinciple (see Figure 4.4)4.3. Example 3: optimization of operational performance4.3.1. General department (top management) ; 4.3.2. Operations departments (middle management); 4.3.3. Operations management (and operationalplayers); Conclusion; Bibliography; Glossary; Index A pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for). Today's decision making can be reached via information (related to the data), knowledge (related to people and processes), and timing (the capacity to decide, act and react at the right time). The huge increase in volume of data traffic, and its format (unstructured data such as blogs, logs, and video) generated by the "digitalization" of our world modifies radically our relationship to the space (in motion) and time, dimension and by capillarity, the enterprInformation systems, web and pervasive computing series.Big dataBig data.005.74023Iafrate Fernando1673414MiAaPQMiAaPQMiAaPQBOOK9910815766903321Advances in information systems set4037492UNINA