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1. |
Record Nr. |
UNINA9910780803503321 |
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Autore |
Garland James C. <1942-> |
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Titolo |
Saving alma mater [[electronic resource] ] : a rescue plan for America's public universities / / James C. Garland |
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Pubbl/distr/stampa |
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Chicago, : University of Chicago Press, 2009 |
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ISBN |
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1-282-42646-X |
9786612426469 |
0-226-28388-7 |
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Descrizione fisica |
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1 online resource (291 p.) |
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Disciplina |
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Soggetti |
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Public universities and colleges - United States - Finance |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Where the money comes from -- Market forces in higher education -- Why public universities cannot restrain costs -- The university prime directive -- The faculty are the university -- The cargo cult college -- The blessing and curse of shared governance -- What price shared governance? -- The shape of things to come -- Leadership begins with the trustees -- The role of governing boards in the new era -- Recruiting presidential leadership -- Reforming the academic culture -- A proposal for deregulation of public universities. |
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Sommario/riassunto |
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America's public universities educate 80% of our nation's college students. But in the wake of rising demands on state treasuries, changing demographics, growing income inequality, and legislative indifference, many of these institutions have fallen into decline. Tuition costs have skyrocketed, class sizes have gone up, the number of courses offered has gone down, and the overall quality of education has decreased significantly. Here James C. Garland draws on more than thirty years of experience as a professor, administrator, and university president to argue that a |
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2. |
Record Nr. |
UNINA9910349305303321 |
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Titolo |
Big Data Analytics and Knowledge Discovery : 21st International Conference, DaWaK 2019, Linz, Austria, August 26–29, 2019, Proceedings / / edited by Carlos Ordonez, Il-Yeol Song, Gabriele Anderst-Kotsis, A Min Tjoa, Ismail Khalil |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XIII, 321 p. 164 illus., 80 illus. in color.) |
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Collana |
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Information Systems and Applications, incl. Internet/Web, and HCI ; ; 11708 |
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Disciplina |
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Soggetti |
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Database management |
Data mining |
Computer arithmetic and logic units |
Computer system failures |
Artificial intelligence |
Database Management |
Data Mining and Knowledge Discovery |
Arithmetic and Logic Structures |
System Performance and Evaluation |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Applications -- Detecting the Onset of Machine Failure Using Anomaly Detection Methods -- A Hybrid Architecture for Tactical and Strategic Precision Agriculture -- Urban analytics of big transportation data for supporting smart cities -- Patterns -- Frequent Item Mining When Obtaining Support is Costly -- Mining Sequential Pattern of Historical Purchases for E-Commerce Recommendation -- Discovering and Visualizing Efficient Patterns in Cost/Utility Sequences -- Efficient Row Pattern Matching using Pattern Hierarchies for Sequence OLAP -- Statistically Significant Discriminative Patterns Searching -- RDF and |
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Streams -- Multidimensional Integration of RDF datasets -- RDFPartSuite: Bridging Physical and Logical RDF Partitioning -- Mining quantitative temporal dependencies between interval-based streams -- Democratization of OLAP DSMS -- Big Data Systems -- Leveraging the Data Lake - Current State and Challenges -- SDWP: A New Data Placement Strategy for Distributed Big Data Warehouses in Hadoop -- Improved Programming-Language Independent MapReduce on Shared-Memory Systems -- Evaluating Redundancy and Partitioning of Geospatial Data in Document-Oriented Data Warehouses -- Graphs and Machine Learning -- Scalable Least Square Twin Support Vector Machine Learning -- Finding Strongly Correlated Trends in Dynamic Attributed Graphs -- Text-based Event Detection: Deciphering Date Information Using Graph Embeddings -- Efficiently Computing Homomorphic Matches of Hybrid Pattern Queries on Large Graphs -- Databases -- From Conceptual to Logical ETL Design using BPMN and Relational Algebra -- Accurate Aggregation Query-Result Estimation and Its Efficient Processing on Distributed Key-Value Store. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 21st International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2019, held in Linz, Austria, in September 2019. The 12 full papers and 10 short papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: Applications; patterns; RDF and streams; big data systems; graphs and machine learning; databases. |
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