Binary is found in Nature Electricity Magnetism Light http://nerdbusiness.com/blog/programming-wire-light-bulbs-battery/ http://www.extremetech.com/computing/113237-ibm-stores-binary-data-on-12-atoms https://delightlylinux.wordpress.com/2014/09/05/binary-lesson-7-bits-and-bytes/
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ADDRESS MAPPING (MULTIPLE CHANNELS) C Row (14 bits) Bank (3 bits) Column (11 bits) Byte in bus (3 bits) Row (14 bits) C Bank (3 bits) Column (11 bits) Byte in bus (3 bits) Row (14 bits) Bank (3 bits) Column (11 bits) Byte in bus (3 bits) Row (14 bits) Bank (3 bits) C Column (11 bits) C Byte in bus (3 bits) • Where are consecutive cache blocks? C Row (14 bits) High Column Bank (3 bits) C High Column Bank (3 bits) High Column C Bank (3 bits) High Column Bank (3 bits) High Column 8 bits Low Col. Byte in bus (3 bits) C Low Col. Byte in bus (3 bits) 3 bits 8 bits Row (14 bits) Byte in bus (3 bits) 3 bits 8 bits Row (14 bits) Low Col. 3 bits 8 bits Row (14 bits) Byte in bus (3 bits) 3 bits 8 bits Row (14 bits) Low Col. Bank (3 bits) Low Col. C Byte in bus (3 bits) 3 bits 47
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Term T# Pos 2015 1 13: 2015 1 AAPL 2 AAPL 2 AAPL 2 AAPL 2 0 1 00001 16: 1 00100 2: 0 14: 1 00001 0 01000 10000 16: 1 00100 01000 0 01000 01000 0 01000 01000 0 01000 6: 0 1 alert 4 00001 00100 01000 01000 00001 3: 0 0 0 0 01000 1 00100 00001 0 00001 00100 00100 01000 01000 00100 00100 00010 01000 00001 00100 01000 01000 00100 01000 01000 01000 7: 0 12: 0 1 5 1 0 00100 1 00011 1 All 0 00100 6: 0 00100 16: 5 1 01000 13: All 0 0 01000 Alert 4 5 0 0 00100 Alert 4 All 0 5: 0 0 0 0 0 1 0 1 0 0 0 AAPL20 Document 2lev pTrees (indexed by Term, Position; Predicate=npz; strides=5) check 13 1: 1 00100 00100 1 1 course14 3: course14 6: course14 7: 1 course14 10: 0 course14 16: 1 00001 01000 0 01000 00100 0 01000 00100 01000 01000 01000 1: 1 customer15 10: customer15 1 Apple 6 16: 1 00100 Area 7 7: 0 Area 7 13: 1 Area 7 16: 1 01000 0 0 01000 0 01000 01000 Day 16 16: 1: 0 01000 1 01000 01000 Big 8 13: Big 8 16: 0 0 00011 0 00100 00100 0 01000 00001 0 01000 00100 01000 01000 01000 Bond 9 13: 1 Bond 9 16: 1 Break 10 10: 0 Break 10 13: 1 Break 10 16: 1 0 0 0 0 0 0 00001 00100 01000 01000 00100 01000 01000 01000 0 0 1 0 0 01000 0 01000 01000 0 01000 00100 00100 01000 10000 00100 01000 01000 01000 0 01000 Happy 18 0 Happy 18 13: 1 0 0 1 01000 01000 00001 00100 01000 01000 01000 4: 0 00100 1 00001 Chart 12 16: 1 00100 Chart 12 13: 0 00011 0 00100 0 01000 1 00001 0 01000 0 01000 0 0 00001 1 00100 01000 0 01000 01000 0 01000 01000 0 01000 0 Happy 18 16: helpin19 11: 0 1 0 01000 0 01000 0 01000 10000 1 00001 helpin19 16: 1 00100 helpin19 13: Her 20 8: 1 Her 20 13: 0 0 1 Her 20 16: 0 0 0 01000 0 01000 0 01000 01000 0 01000 0 01000 0 00001 1 00100 01000 0 01000 1 01000 01000 01000 00001 1 00100 01000 0 01000 01000 0 01000 01000 0 01000 0 https://t.co/5rssX3gnPY 21 8: 0 1 1 00011 https://t.co/DOTIjn3zTh 22 7: 0 https 22 13: 1 0 01000 0 01000 00001 1 00100 1 01000 0 0 0 0 00100 0 01000 0 01000 00100 0 01000 0 01000 00001 1 00100 https 22 16: 0 00100 0 0 01000 00001 1 0 01000 00100 https://t.co/OSDAgXzNFf 26 17: 1 0 01000 00100 https 26 16: 0 1 10000 0 01000 0 01000 0 01000 0 01000 0 10000 00001 0 01000 0 01000 0 00001 0 01000 0 01000 0 01000 0 01000 0 00001 https 27 :13 1 0 01000 00001 1 0 01000 00100 https://t.co/vvdQfCSmj5 27 20: 0 0 01000 00100 https 27 16: https://t.co/XRD1mBpDdE 28 6: 1 00001 1 00001 https 28 16: 1 00100 https://t.co/ZcZhF2AWSE 29 7: 0 https 12: 0 00100 1 00001 https 29 14: 0 00100 https 29 16: 1 00100 https 29 13: 0 01000 0 01000 0 01000 1 00100 0 00011 0 01000 0 00100 0 01000 0 01000 0 01000 0 10000 0 00100 0 01000 0 01000 00100 0 1 00100 0 01000 1 00010 0 01000 http://t.co/oZKXEI87ZK 30 11: 0 0 01000 00100 https 30 13: 1 0 01000 00001 https 30 16: 1 0 01000 00100 01000 0 01000 0 01000 http://t.co/SkOpaK2vQS 32 13: 1 0 01000 00001 1 0 01000 00100 01000 0 01000 00001 http://t.co/u8VxUQotTw 32 13: 1 0 01000 00001 https 32 16: 1 0 01000 00100 https 32 18: 0 0 00011 00100 01000 0 01000 1 00001 1 0 01000 0 01000 1 10000 0 00001 0 01000 0 01000 0 0 00001 0 01000 0 01000 0 01000 0 00001 0 01000 0 01000 0 10000 0 00100 0 1 01000 01000 0 0 01000 01000 1 0 00001 01000 11 16: 0 01000 00100 Call 1 1: https 21 16: 0 1 01000 01000 0 0 01000 01000 0 0 00100 00100 5: 0 00100 0 01000 00001 11 0 01000 0 01000 https 21 13: Call 1 00100 0 01000 00010 0 0 0 00100 1 00001 01000 0 0 1 00100 0 0 00011 0 01000 00100 0 0 01000 1 00001 0 01000 1 0 00100 0 01000 0 01000 0 0 00100 1 00001 1 0 0 01000 1 1 0 00100 https 28 :13 0 00100 0 0 0 01000 01000 0 0 01000 16 15: 01000 8: https 26 :13 01000 Day 00100 8 0 00100 16 13: 00001 0 https 25 16: 00100 0 1 16 00010 0 1 00001 Day 01000 0 0 0 0 00010 customer15 13: Day 00100 0 0 0 0 01000 0 01000 0 01000 https 25 :13 01000 0 00100 Big Chart 12 0 0 0 00100 1 00001 https 24 16: 1 00100 https 24 :13 0 01000 Enail 17 16: 0 01000 0 0 00100 Email 17 13: 0 0 https://t.co/JCchDkM3kZ 24 10: 1 https://t.co/jGJNzj6I5i 25 7: 0 01000 Email 17 10: 00100 0 01000 01000 1 0 00100 0 01000 01000 00001 0 0 00100 01000 13: 01000 0 00100 course14 13: 1 00001 00100 Apple 6 01000 0 01000 0 0 01000 01000 0 00001 00001 0 01000 0 01000 01000 0 01000 01000 0 01000 0 0 01000 0 01000 01000 0 00100 10000 0 00100 1 0 01000 0 01000 0 00001 1 00100 00100 13: 1 00001 16: 0 01000 1 00001 Check 13 16: 0 0 1 Check 13 13: 1 01000 0 01000 16: alert 4 0 3 stride 0 01000 0 01000 01000 0 01000 ahead 3 alert 4 0 2 0 01000 0 01000 00100 13: 1 00001 ahead 3 alert 4 1 0 00100 0 01000 01000 0 01000 0 0 00100 0 01000 0 01000 01000 0 01000 https 32 16: 1 1 0 00001 0 01000 0 00001 0 01000 0 00001 00001 Hype 33 13: Hype 33 16: 1 00001 1 00100 0 01000 0 01000 0 01000 0 01000 0 01000 0 01000 0 00001 0 01000 0 01000 https://t.co/gbgTQasq22 23 9: 0 1 01000 https 23 13: 1 https 23 16: 1 00001 00100 0 0 01000 0 0 0 01000 0 00100 0000134 Info 0 Info 34 13: 0 Info 34 16: 01000 3: 1 00100 1 00001 1 0 01000 0 01000 0 0 01000 0 01000 0 0 01000 0 01000 0
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Example: Reliability Analysis A B C D Start Rank 1 2 3 4 5 E Fail Rank 2 1 3 5 4 1 bution2of battery lifetimes (lognormal) 3 Mean 20 4 Stdev 5 5 6 7 Simulation 8 Failure# Insert Battery Current time In Position 1 In Position 2 9 0 Battery A Battery B 10 1 Battery C 12.017 Battery A Battery C 11 2 Battery D 25.275 Battery D Battery C 12 3 Battery E 29.016 Battery D Battery E 13 4 (none) 52.542 Battery D (none) Decision Models -- Prof. Juran F Battery Battery A Battery B Battery C Battery D Battery E G Life 25.28 12.02 17.00 33.15 23.53 H Start Time 0.0 0.0 12.0 25.3 29.0 12.02 25.28 29.02 52.54 <---- Device Fails I Fail Time 25.3 12.0 29.0 58.4 52.5 16
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A B C D Start Rank 1 2 3 4 5 E Fail Rank 2 1 4 3 5 F G H Battery Life Start Time =RANK(I2,$I$2:$I$6,1) Battery A 28.23 0.0 Battery B 26.41 0.0 Battery C 25.67 26.4 Battery D 22.52 28.2 Battery E 28.99 50.7 I Fail Time 28.2 26.4 52.1 50.7 79.7 J 1 istribution 2of battery lifetimes (lognormal) 3 Mean 20 4 Stdev 5 5 6 =F2 =F3 7 Simulation =VLOOKUP(F4,$B$10:$C$12,2,0) =H4+G4 8 Failure# Insert Battery Current time In Position 1 In Position 2 9 0 Battery A Battery B =F10 =(D10=F2)*(I3)+(E10=E9)*(I2) 10 1 Battery C 26.407 Battery A Battery C 26.41 =IF(MIN(I2:I3)=I3,$F$4,$F$3) 11 2 Battery D 28.229 =IF(MIN(I2:I3)=I2,$F$4,$F$2) Battery D Battery C 28.23 12 3 Battery E 50.750 Battery E Battery C 50.75 13 4 (none) 52.077 Battery E (none) 52.08 <---- Device Fails 14 =IF(((VLOOKUP(E12,$F$2:$I$6,4,0))<(VLOOKUP(D12,$F$2:$I$6,4,0))),(D12),(B13)) 15 16 =IF(((VLOOKUP(E12,$F$2:$I$6,4,0))<(VLOOKUP(D12,$F$2:$I$6,4,0))),(B13),(E12)) 17 18 =(E13=B13)*(VLOOKUP(E12,$F$2:$I$6,4,0))+(E13=E12)*(VLOOKUP(D12,$F$2:$I$6,4,0)) 19 Decision Models -- Prof. Juran 4
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A B C D E 1 Start Rank Fail Rank stribution2of battery lifetimes (lognormal) 1 2 3 Mean 20 2 1 4 Stdev 5 3 3 5 4 5 6 5 4 7 Simulation 8 Failure# Insert Battery Current time In Position 1 In Position 2 9 0 Battery A Battery B 10 1 Battery C 12.017 Battery A Battery C 11 2 Battery D 25.275 Battery D Battery C 12 3 Battery E 29.016 Battery D Battery E 13 4 (none) 52.542 Battery D (none) Decision Models -- Prof. Juran F Battery Battery A Battery B Battery C Battery D Battery E G Life 25.28 12.02 17.00 33.15 23.53 H Start Time 0.0 0.0 12.0 25.3 29.0 12.02 25.28 29.02 52.54 <---- Device Fails I Fail Time 25.3 12.0 29.0 58.4 52.5 11
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P16065 Antisense Copies of Light-Regulated Genes in Rice Abstract T Mike1, N Joshi1, J Bird1, K Margavage1,2, Bryant Morocho1, X-W Deng2, W Terzaghi1,2 Natural antisense transcripts (NATs) are RNAs complementary to sense RNAs that are known to play roles in gene regulation. We studied 21 genes with NAT that are involved in light-regulated pathways in Nipponbare rice (Oryza sativa japonica). Of these genes, 17 were detected by RT-PCR in shoots and roots of Nipponbare seedlings. RTPCR of the Os12g17600 rbcS gene detected multiple small antisense fragments rather than one continuous RNA. Quantitative RT-PCR of Os12g17600 identified 5-fold more sense than NAT in shoots of seedlings grown in light, but 14-fold more sense than NAT in darkgrown seedlings. qRT-PCR of the Os03g51030 PHYA gene indicated that all light treatments decreased the ratio of sense to antisense with the exception of far-red light, which increased the ratio. Several genes exhibited reciprocal regulation of NAT and sense RNAs according to light treatment. Low molecular weight RNA blots of the Os03g07300/ Os03g07310 gene pair identified a small RNA (~40 nucleotides) that was only observed in light-treated roots. These small RNAs might be used to down-regulate the expression of genes turned on by light in roots. 1 Wilkes University, Wilkes-Barre, PA - 2YaleUniversity, New Haven, CT Os03g07300/ Os03g07310 (ribulose-3-P epimerase/ axi protein) Os02g05830 (rbcS2) Conclusions  17 of 21 light-regulated genes examined have NATs, 5 of which are regulated by light  Several genes show reciprocal regulation of mRNA and NAT  Some NATs were processed into small RNAs which may help regulate sense/ antisense RNA transcription  qRT-PCR detected:  ~14-fold more rbcS mRNA than NAT transcripts in shoots grown in continuous darkness  ~5-fold more mRNA than NAT in shoots grown in continuous light  Roots grown 4hr in white light do not increase expression of rbcS mRNA  phyA NAT expression greatly increased upon exposure to white light for 4 Hr or to1mmol. m2 red light. Introduction Discussion  Natural Antisense Transcripts (NATs) RNA molecules complementary to other “sense” RNAs  Present in a variety of organisms, NATs are involved in RNA editing, genomic imprinting, viral defense, etc.  Sense-antisense RNA pairs may reciprocally regulate each other’s production: when production of one transcript increases, production of the other decreases  Prevalence of NATs in plants suggests that NATs may help regulate light responses  Light responses are regulated by complex networks of transcripts  Some antisense RNA is involved in circadian rhythms  Although NATs have been identified in model plant species, their functions are not clear  Tiling-path microarrays identified thousands of genes with NATs   17 of 21 NATs of light-regulated genes found by microarrays were confirmed, validating this high-throughput approach.  Reciprocal light regulation of sense and antisense transcripts was detected for several genes, providing a potential mechanism for regulating the abundance of specific transcripts in response to light. Figure 2: The Os03g07300/ Os03g07310 (ribulose-3-P epimerase/ axi protein) gene pair. A) Low molecular weight Northern showing a 40 nt, root-specific RNA derived from Os03g07310. B) RT-PCR confirming the presence of mRNA of both genes in the leaf tissues. LL: light-grown leaf; DL: dark-grown leaf.  Overlapping NATs initiated from several start sites were identified for Figure 3: Reciprocal regulation of Os02g05830 (rbcS2). In tissues expressing higher levels of NAT, the mRNA is found at lower levels, and vice versa, indicating reciprocal regulation. Os12g17600  Suggests that antisense is not initiated from a single promoter.  Small RNAs derived from several overlapping gene pairs were detected, which may help regulate their expression. LL: light leaf; DL: dark leaf; LR: light root; DR: dark root; 4hr. WL: 4 hour white light; 4hr. WR: 4 hour white root; RL: red leaf; RR: red root; FRL: far red leaf; FRR: far red root; BL: blue leaf; BR: blue root.  NATs are induced in greater magnitudes than sense mRNA in both rbcS and phyA leaves under various light treatments  Questions that still need answers:   Os03g51030 (PHYA) Os12g17600 (rbcS)    Which photoreceptors are involved? Do NATs help modulate light-regulated gene expression? How is NAT/ mRNA production regulated? Are NATs polyadenylated? Sequence of 40nt RNA product of 07300 gene? Methods Figure 1: Antisense and light regulation. High-throughput techniques identified large numbers of antisense and lightregulated transcripts. This research tested the hypothesis that antisense may play a role in light regulation.  Identified antisense transcripts from light-regulated genes in Japonica rice  Query microarray, MPSS, and cDNA databases  Treated seedlings to a variety of lighting conditions to determine effect on mRNA and antisense transcription Plants were grown: 10 days continuous white light or continuous darkness  10 days continuous darkness followed by 4 hours white light  10 days continuous darkness followed by either 1 mmol red light, 1 mmol far red light, or 1 mmol blue, then far red  RNA was extracted from roots and leaves using Ambion’s miRvana Total RNA Isolation kit.  Detection of mRNA and antisense utilized:  Northern blots to verify presence of RNA  Reverse Transcription using the 5’ or 3’ primer only  Real time PCR to quantify relative expression   Expression of Sense and Antisense phyA Transcripts Relative to DLAS in Shoots B) Table 1: Strength of detected antisense signals. The gene pairs in the red box overlap at their annotated 3’ ends and the snoRNA are transcribed from the opposite strands of RPT2 exons. The antisense strands of the remaining genes have no annotated functions. Blue Dark Far Red Light Red 4 hr White Antisense Sense Ratio S:A 1.2 156.0 130.9 1.0 152.1 152.1 0.8 132.2 172.6 2.0 81.9 41.8 4.9 112.3 23.0 2.5 186.9 75.3 B) Expression of Sense and Antisense rbcS Transcripts Relative to DLAS in Shoots Blue Dark Far Red Light Red 4 hr White Antisense 5.8 1.0 Sense Ratio S:A 85.3 14.7 13.9 13.9 5.5 100.6 11.0 57.4 527.3 122.5 10.4 5.2 11.2 40.2 284.0 7.1 Expression of Sense and Antisense rbcS Transcripts Relative to DRAS in Roots Blue Dark Far Red Light Red 4 hr White Antisense 0.4 1.0 Sense Ratio S:A 5.5 12.5 9.8 9.8 1.2 2418.4 153.8 15.7 16842.5 1854.2 12.9 7.0 12.1 0.1 2.5 19.4 Figure 4: Induction of Os03g51030 (phyA) in seedling shoots by various light treatments. Figure 5: Induction of Os12g17600 (rbcS) in seedling shoots by various light treatments. A) Graph of level of induction of sense and antisense standardized to the corresponding dark sample. A)Graph of level of induction of sense and antisense standardized to the corresponding dark sample. B) Expression levels of sense and antisense RNA in shoots relative to dark shoot antisense along with the ratio of sense to antisense. B)Expression levels of sense and antisense RNA in shoots (left) relative to dark shoot antisense and roots (right) relative to dark root antisense along with the ratio of sense to antisense. Acknowledgements This research was primarily supported by NSF grant DBI-0421675: Virtual Center for Analysis of Rice Genome Transcription (XingWang Deng, PI). Additional support from Wilkes University, Yale University, and the Howard Hughes Medical Institute is also gratefully acknowledged.
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Payroll Processing PAY ID PAY PERIOD DATES 6/1/2014 MO7 6/30/2014 5/19/2014 MS7 6/18/2014 6/02/2014 – SP13 6/18/2014 6/19/2014 SP14 7/1/2014 5/25/2014 BW13 6/07/2014 6/08/2014BW14 6/21/2014 6/22/2014 – BW15 7/05/2014 SPECIAL  CHECK DATE BANNER ENCUMBRANC INTERFACE E RELEASE DATE DATE NHIDIST LOAD DATES 7/01/2014 7/01/2014 6/30/2014 06/30/2014 7/01/2014 7/01/2014 6/30/2014 06/27/2014 7/01/2014 7/01/2014 6/30/2014 06/27/2014 7/15/2014 7/15/2014 6/18/2014 06/13/2014 7/02/2014 7/02/2014 06/30/2014 06/30/2014 FY2015 Prepaid Insurance FY2015 Prepaid Insurance FY2015 7/16/2014 7/16/2014 07/11/2014 FY2015 7/15/2014 7/15/2015 07/11/2014 FY2015 6/18/2014 6/18/2014 07/11/2014 COMMENTS Prepaid Insurance FY2015
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SIMPLE NETWORK MANAGEMENT PROTOCOL SNMP uses ASN.1 to format communications between managers and agents, as shown at right. 30 29 Type Type 48: 48: Length: Length: Sequence Sequence41 41 Bytes Bytes 04 06 01 00 Type Type 2: 2: Length: Length: Version: Version: Integer 0 Integer 1 1 Byte Byte 0 70 75 62 Type Type 4: 4: Length: Length: String String 6 6 Bytes Bytes A0 Length: Length: getreq. getreq. 28 Bytes 28 Bytes 01 Type Type 2: 2: Length: Length: Integer Integer 1 1 Byte Byte 30 02 2B 06 04 05 69 AE Type Type 2: 2: Length: Length: Integer 4 Integer 4 Bytes Bytes 00 02 Status Status 0E Type Type 48: 48: Length: Length: Sequence Sequence14 14 Bytes Bytes 6C 63 Value: Value: “public” “public” 1C 02 This particular SNMP message is a request for the sysDescr data item. 02 01 00 06 0C Error Error Index Index 08 Type Type 48: 48: Length: Length: Sequence Sequence12 12 Bytes Bytes Object Object Length: Length: ID 8 ID 8 Bytes Bytes 01 01 02 01 02 Request Request ID ID Type Type 2: 2: Length: Length: Integer Integer 1 1 Byte Byte 30 56 01 00 Data Data Item Item sysDescr sysDescr (numeric (numeric object object identifier identifier 43 43 .. 6 6 .. 1 1 .. 2 2 .. 1 1 .. 1 1 .. 1 1 .. 0 0 05 Null Null CS 00 Length: Length: 0 0 Bytes Bytes Chapter 9 Page 6
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Verbose Output 16 Latency Details: Result Lat PASSED 3.73 PASSED 3.34 PASSED 3.81 PASSED 3.79 PASSED 3.98 Dev Host (rank) <-> -4.5% IBM-3550 (10) <-> -14.4% IBM-3550 (10) <-> -2.5% IBM-3550 (10) <-> -3.0% IBM-3550 (10) <-> +1.9% IBM-3550 (10) <-> Host (rank) st125 (0) st999 (1) IBM-3455 (2) IBM-3655 (3) IBM-3755 (4) Bandwidth Details: Result BW PASSED 838.0 PASSED 947.9 PASSED 946.7 PASSED 873.0 PASSED 947.6 Dev Host (rank) -9.9% IBM-3550 (10) +1.9% IBM-3550 (10) +1.8% IBM-3550 (10) -6.1% IBM-3550 (10) +1.9% IBM-3550 (10) Host (rank) st125 (0) st999 (1) IBM-3455 (2) IBM-3655 (3) IBM-3755 (4) Sep XX, 2007 QLogic Confidential --> --> --> --> --> -->
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Kilo, Mega, Giga, Tera, Peta Kilobyte (KB): 210 bytes = 1,024 bytes ~ 1,000 bytes Approximate size: one e-mail (plain text) Desktop Example: TRS-80 w/4 KB RAM (1977) Megabyte (MB): 220 bytes = 1,048,576 bytes ~ 1,000,000 bytes Approximate size: 30 phonebook pages Desktop Example: IBM PS/2 PC w/1 MB RAM (1987) Gigabyte (GB): 230 bytes = 1,073,741,824 bytes ~ 1,000,000,000 bytes Approximate size: 15 copies of the OKC white pages Desktop: c. 1997 Terabyte (TB): 240 bytes = 1,099,511,627,776 bytes ~ 1,000,000,000,000 bytes Approximate size: 5,500 copies of a phonebook listing everyone in the world Desktop: ??? (Jan 2009: 32 GB) Petabyte (PB): 250 bytes ~ 1,000,000,000,000,000 bytes Desktop: ??? Hardware Lesson CS1313 Spring 2009 44
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Example Your job is to examine light bulbs on an assembly line. You are interested in finding the probability of getting a defective light bulb, after examining 10 light bulbs. – – – Let X = number of defective light bulbs P (defective) = .15 N = 10 1. Is this a binomial set up? 2. What is the probability that you get at most 2 defective light bulbs? 3. What is the probability that the number of defective light bulbs you find is greater than eight? 4. What is the probability that you find between 3 and 5 defective light bulbs?
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Example Your job is to examine light bulbs on an assembly line. You are interested in finding the probability of getting a defective light bulb, after examining 10 light bulbs. – – – Let X = number of defective light bulbs P (defective) = .15 N = 10 1. Is this a binomial set up? 2. What is the probability that you get at most 2 defective light bulbs? 3. What is the probability that the number of defective light bulbs you find is greater than eight? 4. What is the probability that you find between 3 and 5 defective light bulbs?
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Google Hits (Battery Related):             iPod battery good ~ 13.5 Mill iPod battery bad ~ 900 K iPod nano battery good ~ 3 Mill iPod nano battery bad ~ 785 K iPod shuffle battery good ~ 1.6 Mill iPod shuffle battery bad ~ 230 K iPod shuffle battery price good ~ 2.6 Mill (not a typo) iPod shuffle battery price bad ~ 230 K iPod battery price good ~ 13.5 Mill iPod battery price bad ~ 850 K iPod nano battery price good ~ 3 Mill iPod nano battery price bad ~ 785 K
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DBs, DWs are merging as In-memory DBs: SAP® In-Memory Computing Enabling Real-Time Computing SAP® In-Memory enables real-time computing by bringing together online transaction proc. OLTP (DB) and online analytical proc. OLAP (DW). Combining advances in hardware technology with SAP InMemory Computing empowers business – from shop floor to boardroom – by giving real-time bus. proc. instantaneous access to data-eliminating today’s info lag for your business. In-memory computing is already under way. The question isn’t if this revolution will impact businesses but when/ how. In-memory computing won’t be introduced because a co. can afford the technology. It will be because a business cannot afford to allow its competitors to adopt the it first. Here is sample of what in-memory computing can do for you: • Enable mixed workloads of analytics, operations, and performance management in a single software landscape. • Support smarter business decisions by providing increased visibility of very large volumes of business information • Enable users to react to business events more quickly through real-time analysis and reporting of operational data. • Deliver innovative real-time analysis and reporting. • Streamline IT landscape and reduce total cost of ownership. Product managers will still look at inventory and point-of-sale data, but in the future they will also receive,eg., tell customers broadcast dissatisfaction with a product over Twitter. Or they might be alerted to a negative product review released online that highlights some unpleasant product features requiring immediate action. From the other side, small businesses running real-time inventory reports will be able to announce to their Facebook and Twitter communities that a high demand product is available, how to order, and where to pick up. Bad movies have been able to enjoy a great opening weekend before crashing 2nd weekend when negative word-of-mouth feedback cools enthusiasm. That week-long grace period is about to disappear for silver screen flops. Consumer feedback won’t take a week, a day, or an hour. The very second showing of a movie could suffer from a noticeable falloff in attendance due to consumer criticism piped instantaneously through the new technologies. It will no longer be good enough to have weekend numbers ready for executives on Monday morning. Executives will run their own reports on revenue, Twitter their reviews, and by Monday morning have acted on their decisions. The final example is from the utilities industry: The most expensive energy a utilities provides is energy to meet unexpected demand during peak periods of consumption. If the company could analyze trends in power consumption based on real-time meter reads, it could offer – in real time – extra low rates for the week or month if they reduce their consumption during the following few hours. In manufacturing enterprises, in-memory computing tech will connect the shop floor to the boardroom, and the shop floor associate will have instant access to the same data as the board [[shop floor = daily transaction processing. Boardroom = executive data mining]]. The shop floor will then see the results of their actions reflected immediately in the relevant Key Performance Indicators (KPI). This advantage will become much more dramatic when we switch to electric cars; predictably, those cars are recharged the minute the owners return home from work. Hardware: blade servers and multicore CPUs and memory capacities measured in terabytes. Software: in-memory database with highly compressible row / column storage designed to maximize in-memory comp. tech. SAP BusinessObjects Event Insight software is key. In what used to be called exception reporting, the software deals with huge amounts of realtime data to determine immediate and appropriate action for a real-time situation. [[Both row and column storage! They convert to column-wise storage only for Long-Lived-High-Value data?]] Parallel processing takes place in the database layer rather than in the app layer - as it does in the client-server arch. Total cost is 30% lower than traditional RDBMSs due to: • Leaner hardware, less system capacity req., as mixed workloads of analytics, operations, performance mgmt is in a single system, which also reduces redundant data storage. [[Back to a single DB rather than a DB for TP and a DW for boardroom dec. sup.]] • Less extract transform load (ETL) between systems and fewer prebuilt reports, reducing support required to run sofwr. Report runtime improvements of up to 1000 times. Compression rates of up to a 10 times. Performance improvements expected even higher in SAP apps natively developed for inmemory DBs. Initial results: a reduction of computing time from hours to seconds. However, in-memory computing will not eliminate the need for data warehousing. Real-time reporting will solve old challenges and create new opportunities, but new challenges will arise. SAP HANA 1.0 software supports realtime database access to data from the SAP apps that support OLTP. Formerly, operational reporting functionality was transferred from OLTP applications to a data warehouse. With in-memory computing technology, this functionality is integrated back into the transaction system. Adopting in-memory computing results in an uncluttered arch based on a few, tightly aligned core systems enabled by service-oriented architecture (SOA) to provide harmonized, valid metadata and master data across business processes. Some of the most salient shifts and trends in future enterprise architectures will be: • A shift to BI self-service apps like data exploration, instead of static report solutions. • Central metadata and masterdata repositories that define the data architecture, allowing data stewards to work across all business units and all platforms Real-time in-memory computing technology will cause a decline Structured Query Language (SQL) satellite databases. The purpose of those databases as flexible, ad hoc, more business-oriented, less IT-static tools might still be required, but their offline status will be a disadvantage and will delay data updates. Some might argue that satellite systems with in-memory computing technology will take over from satellite SQL DBs. SAP Business Explorer tools that use in-memory computing technology represent a paradigm shift. Instead of waiting for IT to work on a long queue of support tickets to create new reports, business users can explore large data sets and define reports on the fly.
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