02716nam 22006374a 450 991045751590332120200520144314.00-19-988351-31-280-53452-41-4237-4604-X0-19-803792-91-4337-0081-6(CKB)1000000000362971(EBL)279814(OCoLC)191036877(SSID)ssj0000144481(PQKBManifestationID)11152619(PQKBTitleCode)TC0000144481(PQKBWorkID)10167512(PQKB)11206951(MiAaPQ)EBC279814(MiAaPQ)EBC4704967(Au-PeEL)EBL279814(CaPaEBR)ebr10142527(OCoLC)62866134(EXLCZ)99100000000036297120040130d2004 uy 0engur|n|---|||||txtccrEffective knowledge management for law firms[electronic resource] /Matthew ParsonsOxford ;New York Oxford University Press20041 online resource (261 p.)Includes index.0-19-516968-9 Includes bibliographical references (p. 241-245) and index.It is said that law firm's don't get knowledge management -- What is knowledge management all about? -- The business and economics of law firms -- Lawyers as knowledge workers : what lawyers do -- What is a law firm knowledge strategy? How do you develop one? -- Preparation 101 : culture matters! -- Consultation : agreeing the processes for change management -- Story : the lawyer's life in the new world -- Personal knowledge strategy : tacit is king -- Interpersonal knowledge strategy : creation and projection -- Impersonal and digital knowledge strategy.In Effective Knowledge Management for Law Firms, Matthew Parsons draws on his work with a leading commercial law firm, Mallesons Stephen Jaques. He examines how law firms can implement a knowledge strategy to support their business strategy, rather than getting beguiled by fad and technology.Law firmsUnited StatesManagementLaw officesUnited StatesKnowledge managementUnited StatesElectronic books.Law firmsManagement.Law officesKnowledge management340/.068Parsons Matthew1967-954911MiAaPQMiAaPQMiAaPQBOOK9910457515903321Effective knowledge management for law firms2159767UNINA04846nam 2201225z- 450 991057687810332120220621(CKB)5720000000008394(oapen)https://directory.doabooks.org/handle/20.500.12854/84521(oapen)doab84521(EXLCZ)99572000000000839420202206d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierData Science and Knowledge DiscoveryBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (254 p.)3-0365-4316-3 3-0365-4315-5 Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining.Computer sciencebicsscInformation technology industriesbicsscactivity recognitionadaptation processArcGISartificial intelligenceattributionauthorshipautomationbig dataBig Databox-counting frameworkchatbotsclassificationcontent base image retrievalCOVID-19crisis reportingcustomer relationship management (CRM)dashboarddata analysisdata analyticsdata augmentationdata miningdata sciencedatabasesdecision systemsdeep featuresdeep learningdigital humanitiesdigital infrastructuresdistracted drivingdriving behaviordriving operation areae-commerceeconomic determinants of open dataESP32 microcontrollerfeature extractionforensic intelligencefractal dimensiongeoinformation technologygovernance and social institutionshumanitiesICTinformation systemsinterdisciplinary researchinternet of thingsioCOVID19journalistslinked open dataLoRaWANmachine learningmedia analyticsmedia criticismmultimedia document retrievaln/aneural networksnews mediaopen government dataprediction by partial matchingpublic healthrough setsrule based systemsSARS-CoV-2script Pythonsemantic information retrievalsmart homessocial sciencesspatio-temporalterritorial road networktext miningtextbook researchThe Things NetworkWeb IntelligenceWebGISComputer scienceInformation technology industriesPortela Filipeedt1268012Portela FilipeothBOOK9910576878103321Data Science and Knowledge Discovery3021749UNINA