CiteSeerX — Table search results — Data Management on New Hardware - 7th International Workshop on Accelerating Data Analysis and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016 and 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, New Delhi, India, September 1, 2016, Revised Selected Papers

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Table 1 GWRAPPS datasets, purpose and data type Name Purpose Data Type

in A GIS-based Water Resources and Agricultural Permitting and Planning System (GWRAPPS)

by Sudheer R. Satti, Jennifer M. Jacobs 2003

"... In PAGE 5: ...storing the temporally explicit data in a RDBMS and maintaining appropriate links from a GIS layer to the RDBMS tables. Table1 summarizes the GWRAPPS data storage. 3.... ..."

Table 1: Data management for the coe cients cijk.

in Iterative Solution of Systems of Linear Equations Arising in the Context of Stochastic Finite Elements

by Manuel F. Pellissetti, Roger G. Ghanem 2000

"... In PAGE 6: ... Speci cally, denoting a new pointer vector by l, it is constructed such that, jlm = m; with jlm?1 lt; m: The index m ranges from 1 to nnzcijk, where nnzcijk is the number of non-zero lower diagonal cijk apos;s. Table1 shows this construction. It should be noted that this pointer vector plays the same role as the row-pointer in the Compressed Sparse Row-format.... In PAGE 6: ...ower diagonal cijk apos;s. Table 1 shows this construction. It should be noted that this pointer vector plays the same role as the row-pointer in the Compressed Sparse Row-format. For example and referring to Table1 , the rst entry in cijk that has index j equal to 3 occurs as the 4th entry in the table. Thus the value 4 on the 3rd row of the pointer vector l.... In PAGE 6: ... This means that only those coe cients are stored for which the entries of k do not exceed the corresponding entries of j. Table1 illustrates the data management for a 2-term Karhunen-Loeve expan- sion and a 3rd order Polynomial Chaos representation of the response process. 4 Preconditioning Aspects The particular properties of SFEM-systems have important repercussions on pre- conditioning.... ..."

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Table II depicts a time line of operations when the new data block B is allocated to a le I (x1) and written to disk (x2). VA observes this request and allocates the new extent for B and updates its in-memory block map V (x2-1). VA commits this metadata change to disk (x2-2) before B reaches the disk (x2-3). The le system is consistent despite a crash at any point up to x2-1 because VA does not modify any normal operation of the le system (except the V updates in-memory which will

in An Approach to Virtual Allocation in Storage Systems

by Sukwoo Kang, A. L. Narasimha Reddy

Table 1. Summary statistics of performance and non-performance manager characteristics in the 1987-1994 sample period. Panel A. The 95th through 5th Percentiles in the pension fund manager and mutual fund manager distributions This panel contains the distribution of manager characteristics in the pension fund and mutual fund industry segments over all manager-years used in the analysis of Tables 2-4. The pension fund data is from the June 1995 M-Search Database, distributed by Mobius, Inc. The mutual fund database is from the July 1995 Mutual Funds OnDisc CD distributed by Morningstar, Inc. These managers are from the actively managed domestic equity, domestic growth, and domestic value style categories only. There are 2,461 manager-years in the pension sample and 2,676 manager-years in the mutual fund sample. There are 562 individual pension managers and 483 individual mutual fund managers. All flow and performance variables are on an annual basis and are defined in the Appendix. Due to a lack of pension fund fee data, pension manager returns are gross of management fees, while the mutual fund manager returns are net of management fees and expenses. Pension fund manager distribution Mutual fund manager distribution

in The determinants of the flow of funds of managed portfolios: mutual funds versus pension funds

by Diane Del Guerico, Paula A. Tkac

"... In PAGE 17: ...5. Comparative summary statistics Table1 contains manager-year statistics that highlight some of the basic similarities and differences across the two segments. The distribution in assets under management indicates skewness in both segments, but there are clearly larger asset pools in the pension manager sample.... In PAGE 17: ... We also find that the distribution of manager-years in the broad domestic equity, growth and value style categories is roughly similar in both samples. Panels B and C of Table1 contain pairwise correlation coefficients of our flow and performance variables, estimated separately for each industry segment. The pairwise correlations between performance variables are not high enough to cause concern over multicollinearity problems in our regressions.... In PAGE 30: ... Chen and Pennachi (1999) provide an alternative test based on tracking error. 35 See Ackermann (1997) Table1 and Institutional Investor PensionForum November 1997, p. 59.... In PAGE 39: ...Table1 . Summary statistics (continued) The symbols *, **, and *** indicate statistical significance at the 10, 5, and 1% levels.... ..."

Table 1: Data Elements of a Metadata Management System.

in Ontology based metadata management in medical domains

by Quddus Chong, Anup Marwadi, Kaustubh Supekar, Yugyung Lee

"... In PAGE 5: ... In order to perform quality research that can be published and accepted worldwide, medical organisations need to use data sources that are accepted and recommended by the international community. In Table1 , we identify the types of metadata elements at the database and domain concepts levels. Here, we view a resource as being a conceptual mapping to a set of related entities.... ..."

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Table 1: Data Elements of a Metadata Management System

in Ontology based metadata management in medical domains

by Quddus Chong, Anup Marwadi, Kaustubh Supekar, Yugyung Lee

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TABLE 6-2 Attributes-in-memory vs. Complete-object-in-database Timings

in Architectural Alternatives for Connecting

by Persistent Programming Language, Brian Kennedy, David Maier, Harry Porter

"... In PAGE 98: ...1 using both algorithms, and measured the number of bytes of data transferred between S and GemStone, in both directions. Table 6-4 shows the amount of data transfer for each of the test cases: TABLE6 -4 # of bytes of data transferred between S and GemStone g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Size and Location g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Number and Location g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 S to Gem g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Gem to S g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 S to Gem g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Gem to S g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Test 1 612 1454 612 1454 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Test 2 240 873 240 873 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Test 3 8460 50148 8460 50148 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Test 4 59220 349760 61508 363488 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 g105 Test 5 342452 615999 342452... ..."

Table 1. Comparison of the data managed by client and servers

in MACAO - A Journey into CAx Interoperability and Collaborative Design

by Florian Arnold Research, Florian Arnold

"... In PAGE 3: ... Therefore, new data structures and appropriate access functions have been defined which have been derived from the data structures found in the APIs of the analysed CAD systems. The transferred tessellated (or facetted) data consists of triangles representing faces and of points which define polylines representing curves ( Table1 ). This information is sufficient not only for visualisation but also for interaction because it enables the client to access the associated server-side mathematical description of surfaces and curves if needed.... ..."

Table 6. Degree of use of process quality management

in QUALITY MANAGEMENT: UNIVERSAL OR CONTEXT DEPENDENT?

by Christopher A. Voss

"... In PAGE 32: ... 5.1 Testing for Association between Overall Strategic Context and the Pattern of Use of Practices Table6 summarizes the degree of use of the several process QM practices across plants resulting from the data reduction stage. The plants are ordered according to their relative positions along the strategic context spectrum, as given by the context scores in Table 5.... In PAGE 32: ... The plants are ordered according to their relative positions along the strategic context spectrum, as given by the context scores in Table 5. Take in Table 6 The visual pattern in Table6 strongly suggests that process QM practices are dependent on strategic context. We conducted two complementary statistical analyses to further ... In PAGE 33: ... We used the statistical software STATA (1997) to perform the analyses. The visual pattern in Table6 suggests that the patterns of use of practices in the two Niche Differentiator plants are similar between them (literal replication) and in clear contrast with the patterns observed in the two Cost Leader plants (theoretical replication), which are also similar between them. In addition, it suggests that the use of practices follows a distinct trend as one moves across the strategic spectrum.... In PAGE 34: ...g., Conover, 1999) between a context variable (CTX) categorizing the relative position of each plant across the strategic spectrum as given by Table 5 (CTX takes the value 1 to 5 for plants 1 to 5 respectively) and the degree of use of each practice across the plants (3 (High), 2 (Medium) or 1 (Low)) as given by Table6 . Table 7 shows the results of the correlation analysis.... In PAGE 49: ...Regarding the first possible structural fix - the mix of practices to adopt - the patterns in Table6 can be directly used for process QM. In what concerns the second possible fix - the modification of adverse strategic context characteristics - the study identified critical strategic context characteristics which strongly affect process QM practices, namely, the degree of customization, rate of new product introduction and internal item variety.... ..."

Table 1 Characteristics of the two alert management approaches presented in this paper. Data mining Machine Learning

in Data Mining and Machine Learning---Towards Reducing False Positives

by In Intrusion Detection, Tadeusz Pietraszek A, Axel Tanner A

"... In PAGE 4: ... These two facts form the basis of the new alert-handling paradigm using two orthogonal ap- proaches: data mining and machine learning. The characteristics of the two approaches are pre- sented in Table1 and will be discussed in more detail in the following sections. As a general remark, note that alert- management systems, such as these presented in this paper, process only alerts generated by an IDS and are therefore not capable of detecting attacks that the underlaying IDS missed.... ..."