New New Design Design Options Options Characteristics: Characteristics: • •Breaks Breaksdown down departmental departmentalbarriers. barriers. • •Decentralizes Decentralizesdecision decision making to the team making to the teamlevel. level. • •Requires Requiresemployees employeesto to be generalists as well be generalists as wellas as specialists. specialists. • •Creates Createsaa“flexible “flexible bureaucracy.” bureaucracy.”
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The Contestants… Team 1: Team 2: Team 3: Team 4: Team 5: Team 6: Team 7: Team 8: Team 9: Team 10: Team 11: Team 12: Team 13: Team 14: Team 15: Team 16: Team 17: Team 18: Team 19: Team 20: Team 21: Team 22: Team 23: Team 24: Team 25: Team 26: Team 27: Team 28: Back to the Contest…
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Clustering of S&P 500 Stock Data  Observe Stock Movements every day.  Clustering points: Stock-{UP/DOWN}  Similarity Measure: Two points are more similar if the events described by them frequently happen together on the same day.  We used association rules to quantify a similarity measure. Discovered Clusters Industry Group 1 2 Applied-Matl-DOW N,Bay-Net work-Down,3-COM-DOWN, Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN, DSC-Co mm-DOW N,INTEL-DOWN,LSI-Logic-DOWN, Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down, Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOW N, Sun-DOW N Apple-Co mp-DOW N,Autodesk-DOWN,DEC-DOWN, ADV-M icro-Device-DOWN,Andrew-Corp-DOWN, Co mputer-Assoc-DOWN,Circuit-City-DOWN, Co mpaq-DOWN, EM C-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOW N,Microsoft-DOWN,Scientific-Atl-DOWN 3 4 © Tan,Steinbach, Kumar Fannie-Mae-DOWN,Fed-Ho me-Loan-DOW N, MBNA-Corp -DOWN,Morgan-Stanley-DOWN Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP, Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP, Schlu mberger-UP Introduction to Data Mining Technology1-DOWN Technology2-DOWN Financial-DOWN Oil-UP 4/18/2004 20
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Formative measurement example • Construct – • Team electronic communication use Indicators (question-statements answered on a Likert-type scale) 1. 2. 3. 4. 5. 6. 7. The team used e-mail to fellow team members (1 to 1). The team used e-mail to team distribution lists (1 to many). The team used team messaging boards or team discussion forums. The team used shared electronic files. The team used Lotus notes to facilitate sharing information among team members. The team used electronic newsletters that covered project information. The team used auto routing of documents for team member and management approval. 8. The team used file transfer protocols (FTP) to attach documents to e-mails and Web pages. 9. The team used a Web page dedicated to this project. 10. The team used a Web page for this project that contained project specs, market research information, and test results. 11. The team used voice messaging. 12. The team used teleconferencing. 13. The team used video conferencing 14. The team used desktop video conferencing 15. The team used attached audio files to electronic documents. 16. The team used attached video files to electronic documents.
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Data Management Fire Site         Team #1 Team #2 Team #3 Team #4 Team #5 ONCE YOU HAVE YOUR CONC./GRAM DATA Conc. Cu ((µg/mL)/g) 1.179 0.643 0.712 0.689 0.972 COME UP TO THIS COMPUTER AND ENTER IT IN Conc. Fe ((µg/mL)/g) 2.032 1.298 1.052 1.708 1.135 SO EVERYONE CAN SEE IT!!!!! Conc. Zn ((µg/mL)/g) 0.774 0.772 0.791 0.787 0.805 Suspect #1 Suspect #2   Suspect #3 Suspect #4 Suspect #5 Team #1 Team #2 Team #3 Team #2 Team #6 Team #3 Team #4 Team #4 Team #5 Team #1 Team #5 Conc. Cu ((µg/mL)/g) 0.120 -0.059 0.168 0.176 xxxxxxxx 0.701 0.632 -0.042 0.276 0.120 0.649 Conc. Fe ((µg/mL)/g) 0.012 0.757 0.907 0.820 xxxxxxxx 1.048 1.399 1.661 0.703 1.812 0.868 0.000 -0.053 0.250 xxxxxxxx 0.774 0.678 0.253 0.247 0.405 0.624 G E S Conc. Zn ((µg/mL)/g) Blank #1   0.210   Blank #2   Team #1 Team #2 Team #3 Team #4 Team #5 Conc. Cu ((µg/mL)/g) 0.120 Conc. Fe ((µg/mL)/g) 0.005 0.638 0.497 0.657 0.354 Conc. Zn ((µg/mL)/g) 0.000 -0.024 -0.108 -0.078 -0.014 A -0.054 -0.047 E -0.084 R 0.008 Fire Site V Susp. #1 A Conc. Cu ((µg/mL)/g) 0.839 0.031 0.428 0.666 0.117 0.384 Conc. Fe ((µg/mL)/g) 1.445 0.385 0.559 1.223 1.182 1.340 0.380 0.505 Conc. Zn ((µg/mL)/g) 0.786 -0.027 0.210 0.726 0.250 0.514 -0.044 -0.046 Susp. #2 Susp. #3 Susp. #4 Susp. #5 Blank #1 Blank #2 0.006 -0.038
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21-43 Departmental Departmental Contribution Contribution to to Overhead Overhead Departmental Departmental revenue revenue –– Direct Direct expenses expenses == Departmental Departmental contribution contribution Departmental contribution . . . • Is used to evaluate departmental performance. • Is not a function of arbitrary allocations of indirect expenses. A department may be eliminated when its departmental contribution is negative. McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2005
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Seismic attribute-assisted interpretation of incised valley fill episodes: A case study of Anadarko Basin Red Fork interval. Yoscel Suarez*, Chesapeake Energy and The University of Oklahoma, USA Kurt J. Marfurt, The University of Oklahoma, USA Mark Falk, Chesapeake Energy, USA Al Warner , Chesapeake Energy, USA Abstract Previous Work Discrimination of valley-fill episodes and their lithology has always posed a challenge for exploration geologists and geophysicists, and the Red Fork sands in the Anadarko Basin do not fall outside of this challenge. The goal of this study is to take a new look at seismic attributes given the considerable well control that has been acquired during the past decade. By using this well understood reservoir as a natural laboratory, we calibrate the response of various attributes to a well-understood incised valley system. The extensive drilling program shows that seismic data has difficulty in distinguishing shale episodes vs. sand episodes, where the ultimate exploration goal is to find productive valley fill sands. In 1998 Lynn Peyton, Rich Bottjer and Greg Partyka published a paper in the Leading Edge describing their use of coherency and spectral decomposition to identify valley fill in the Red Fork interval in the Anadarko Basin. Their work help them identify five valley-fill sequences in order to find optimum reservoir intervals and to reduce exploration risk . Due to the discontinuity of the valley-fill episodes the mapping of such events by using conventional seismic displays is extremely challenging. Figure 3 shows one of the stratigraphic well cross-section presented by Peyton et al where the discontinuities of this complex are evident. Figure 4 shows a seismic profile that parallels the wells cross-section highlighting the same stages. The seismic section is flattened in the Novi. Since original work done in 1998 both seismic attributes and seismic geomorphology have undergone rapid advancement. The findings of this work will be applicable to nearby active areas as well as other intervals in the area that exhibit the same challenges. Using Peyton et al’s (1998) work as a starting point we generated similar displays of conventional seismic profiles and well x-sections that will become the bases of our research efforts. Figure 8 shows the geometry and extents of the different episodes of the Red Fork incised valley system based on well data interpretation and conventional seismic displays. This map will be compared to the different seismic attributes to calibrate their response. Figure 9 (a,b) show couple of well x-sections and their corresponding seismic profiles that supported the valley-fill stages map in Figure 8. Seismic attributes have undergone rapid development since the mid 1990s. In lieu of the horizon-based spectral decomposition based on the discrete Fourier transform, we use volumetric-based spectral decomposition based on matched pursuit and wavelet transforms (e.g. Liu and Marfurt,2007) . Other edge-sensitive attributes include more modern implementations of coherence, long-wavelength Sobel filters, and amplitude gradients. Figure 10 shows a horizon slice at the Red Fork level. Note that on conventional data the channel complex is identifiable. However, the use of seismic attributes may help delineate in more detail the different episodes within the same fluvial system and better define channel geomorphology. We will compare different edge detection algorithms and the advantages and disadvantages that each of them provides to the interpreter. Also, matching pursuit spectral decomposition results will be presented as well as combinations of Relative Acoustic Impedance and semblance that provide helpful information in the interpretation of this dataset. The surveys are located in west central Oklahoma. They were shot by Amoco from 19931996 and later merged into a 136 sq.mi. survey. In 1998, Chesapeake acquired many of Amoco’s Mid-continent properties including those discussed by Peyton et al. (1998). In this study we present alternative seismic attribute-assisted interpretation workflows that show the potential information that each of the geometric and amplitude-based attributes offer to the interpreter when dealing with Red Fork valley-fill episodes in the Anadarko Basin. It is important to mention that one of the biggest challenges of this dataset is the acquisition footprint, which contaminates the data and limits the resolution of some of the seismic attributes. Geological Framework Methodology A Figure 3. Stratigraphic cross-section Red Fork valley –fill complex Figure 4. Seismic profile associated to the prior crosssection. Flattened in the Novi interval By generating horizon slices in the coherency volume they were able to identify and delineate the main geometries of the incised valley (Figure 5). The event used to generated the horizon slice is the Skinner Lime above the Red Fork interval. A’ The Pennsylvanian incised valley sequence associated with the Red Fork interval has, throughout most of its extent, three major events or facies (Phase I, II, and III) which can be differentiated by log signatures, production characteristics, and gross geometry. Two additional events (Phase IV and V) are present in the eastern and northeastern headward portion of the valley, also recognizable by log signature and gross geometry. Phase II Phase III Phase V Figure 8. Red Fork incised valley geometries and valley-fill episodes The multi phase events of the Upper Red Fork Valley system were most likely caused by repeated sea level changes resulting from Pennsylvania glacial events that were probably related to the Milankovitch astronomical cycles including the changing tilt of the earth’s axis and eccentricity of the earth’s elliptical orbit. Phase I is the earliest valley event and Phase II generally has a much wider represents the narrow, initial downcutting of the valley sequence. Where present (a considerable portion of Phase I has been eroded by later events), the rocks are generally poorly correlative shales, silts, and tight sandstones overlying a basal “lag” deposit. areal distribution (up to four miles) with a variety of valley fill facies deposited which record a period of valley widening and maturation. Logs over Phase II rocks illustrate a classic fining upward pattern and shale resistivities of 10 or more ohms. Phase III rocks record the last major incisement within the valley and occur within a narrow (0.25-.05 mile wide) steep walled system that is correlative for 70 miles. This rejuvenated channel actually represents the final position of the Phase II river before base level was lowered and renewed downcutting began. Phase III reservoirs are primarily thick, blocky, porous sands at the base of the sequence that have been backfilled, reworked, and overlain by low resistivity marine shales deposited by a major transgression which drowned the valley sequence. Figure 5. Coherency horizon slice at the Red Fork level Phase V the last event before the transgression that deposited the Pink. It’s primary significance is that it either partially or completely eroded much of the Phase III Valley event. Phase V rocks are poorly developed, non productive sand and shales which also have a characteristic log signature. end of Phase III marine shale deposition. Phase IV rocks are characterized by thin, tight, interbedded sands and shales with a coal or coaly shale near the base. This facies is interpreted as an elongated lagoon/ coal swamp or possibly bay head delta as it often extends beyond the confines of the deeper valley. The Induction log signature is a very distinct “serrated” pattern with a “hot” gamma ray near the base identifying the coal or coaly shale. Pink Lime In their workflow they also estimated the spectral decomposition. They found that the 36 Hz component best represented the different valley-fill stages (Figure 6). By combining the well-data with the information from the seismic attributes they were able to delineate the extents of the different valley –fill episodes (Figure 7) and generate and integrated interpretation of the system. Lower Red Fork II III II Middle Red Fork V a) Figure 9. a) Red Fork stratigraphic cross-section. b) Seismic profile showing the stratigraphic interpretation derived from the well data Phase IV records a modest regression at the The geological framework summary is courtesy of Al Warner. Senior Geologist at Chesapeake Energy Figure 10. Conventional seismic horizon slice at the Red Fork level. The channel discernible although signal/noise ratio is affected by acquisition footprint Figure 6. Spectral decomposition (36 Hz) horizon slice at the Red Fork level Figure 7. Spectral decomposition (36 Hz) horizon slice at the Red Fork level with interpretation. III b) II V
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Integrated Student Experience Organizational Chart of Work Teams NECC 2020 Strategic Planning Steering Committee Integrated Student Experience Goal Team Student Career Opportunities Goal Team Professional Growth Goal Team External Partnerships Goal Team Integrated Student Experience Implementation Alliance Guided Curriculum Pathways Team Meta Major Centers Team Advising in the Hub Team Academic Advising Council Entry/First Year Team Student Success Hub Core Team Career/Exploratory Track/FYS Team EAB Navigate Team New Student Orientation Team Student Support Services Team Meta Major Center Transition Team
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Classification   How are Continuous Data Categorized in Symbology? Classification Methods ◦ Equal Interval/Defined Interval  Place Breaks at Equal Intervals, Specifying Number or Width of Breaks ◦ Standard Deviation  Place Breaks at Equal Standard Deviations From the Mean Value ◦ Quantile  Place Breaks Such That Groups Have Equal Size Memberships ◦ Natural Breaks  Place Breaks Between Clusters of Data ◦ Manual Breaks
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Classification  How are Continuous Data Categorized in Symbology?  Classification Methods ◦ Equal Interval/Defined Interval  Place Breaks at Equal Intervals, Specifying Number or Width of Breaks ◦ Standard Deviation  Place Breaks at Equal Standard Deviations From the Mean Value ◦ Quantile  Place Breaks Such That Groups Have Equal Size Memberships ◦ Natural Breaks  Place Breaks Between Clusters of Data ◦ Manual Breaks
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