Aggregation computation over distributed data streams
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The document discusses aggregation computation over distributed data streams, focusing on challenges like duplicate-resilient aggregates in large-scale monitoring applications. It introduces the Flajolet-Martin sketch algorithm as a probabilistic method for distinct counting in multi-sets with limited memory and processing power. Key considerations include communication, computational, and space costs associated with these algorithms in distributed environments.
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![Flajolet-Martin Sketch(Cont.) Preliminary what do we need? the Multi-set M, containing all items/records, and |M| = n the upper bound on the number of distinct items/records U, which is more than n the bitmap B, consisting of L elements, and 2 L = U the hash function h(x: item/record), transforming each items into a binary string distributed uniformly over the range of [1…2 L ], just like b 1 b 2 …b L , in which b 1 is the lowest digit, and b L is the highest the p(x), attaining the left most position of ‘1’ 12/15/11 Middleware, CCNT, ZJU counting not computing 1 1 … 0 B 0 PPT VS Whiteboard ? x record h(x) 1 L](https://image.slidesharecdn.com/aggregationcomputationoverdistributeddatastreams-111215074354-phpapp01/85/Aggregation-computation-over-distributed-data-streams-9-320.jpg)
![Flajolet-Martin Sketch(Cont.) The algorithm itself the core task: Remarking the position of which the leftmost ‘1’ of the hash value recorded by p(x) in bitmap B 12/15/11 Middleware, CCNT, ZJU for i:=1 to L do bitmap[i] :=0 for all x in M do begin index := p(hash(x)); if bitmap[index] = the bitmap[index] :=1; end Why?](https://image.slidesharecdn.com/aggregationcomputationoverdistributeddatastreams-111215074354-phpapp01/85/Aggregation-computation-over-distributed-data-streams-10-320.jpg)
![Flajolet-Martin Sketch(Cont.) The explanation The fact: bitmap[k] equals to 1 iff after execution a pattern of the form 0 k-1 1 has appeared amongst hashed values of records in M The probability: the occurrence probability of the pattern 0 k-1 1 is 1/2 k Occurrence times: so if |M| = n, then bitmap[1] is accessed approximately n/2 times, bitmap[2] approximately n/4 times Extension: bitmap[k] will almost certainly be zero if k >> log 2 (n) and one if k << log 2 (n) wit a fringe of 0 and 1 for k ≈ log 2 (n) Selection: the leftmost 0, the rightmost 1 or something else 12/15/11 Middleware, CCNT, ZJU U The most practical part is over, and the left is very complicated taken for proving and error analysis, namely all about mathematic for i:=1 to L do bitmap[i] :=0 for all x in M do begin index := p(hash(x)); if bitmap[index] = the bitmap[index] :=1; end](https://image.slidesharecdn.com/aggregationcomputationoverdistributeddatastreams-111215074354-phpapp01/85/Aggregation-computation-over-distributed-data-streams-11-320.jpg)
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[Please copy the link and paste it into any web browser to access the content.]
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The Texas outbreak has slowed down, but sporadic cases continue to emerge in Kansas, Oklahoma, and New Mexico.
Elsewhere in the US, we continue to see signs of acceleration due to outbreaks outside the Southwest (North Dakota, Montana, and Colorado) and travel-related cases. Measles exposures due to travel are expected to pose a significant challenge throughout the summer.
The U.S. is on track to exceed its 30-year high for measles cases (1,274) within the next two weeks.
Here is the latest update:
CURRENT CASE COUNT: 919
•Texas: 744 (+2) (55% of cases are in Gaines County).
•New Mexico: 81 (83% of cases are from Lea County).
•Oklahoma: 20 (+2)
•Kansas: 74 (+5) (38.89% of the cases are from Gray County).
HOSPITALIZATIONS: 104
• Texas: 96 (+2) – This accounts for 13% of all cases in Texas.
• New Mexico: 7 – This accounts for 9.47% of all cases in New Mexico.
• Kansas: 3 – This accounts for 5.08% of all cases in the state of Kansas.
DEATHS: 3
•Texas: 2 – This is 0.27% of all cases in Texas.
•New Mexico: 1 – This is 1.23% of all cases in New Mexico.
US NATIONAL CASE COUNT: 1,197
INTERNATIONAL SPREAD
•Mexico: 2337 (+257), 5 fatalities
‒Chihuahua, Mexico: 2,179 (+239) cases, 4 fatalities, 7 currently hospitalized.
•Canada: 3,207 (+208), 1 fatality
‒Ontario Outbreak, Canada: 2,115 (+74) cases, 158 hospitalizations, 1 fatality.
‒Alberta, Canada: 879(+118) cases, 5 currently hospitalized.
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♥Our Monthly Class Roster is 7,141 for 6/21.
ALL students get privacy naturally. Thx Everyone.
♥ Coming to our Shop This Weekend.
Timeless for Future Grad Level Students.
Practitioner Student. Level/Session 2 Packages.
* ♥The Review & Topics:
* All virtual, adult, education students must be over 18 years to attend LDMMIA eClasses and vStudio Thx.
* Please refer to our Free Workshops anytime for review/notes.
* Orientation Counts as S1 on introduction. Sold Separately as a PDF. Our S2 includes 2 Videos within 2 Mp4s. Sold Separately for Uploading.
* Reiki Is Japanese Energy Healing used Globally.
* Yoga is over 5k years old from India. It hosts many styles, teacher versions, and it’s Mainstream now vs decades ago.
* Teaching Vod, 720 Res, Mp4: Yoga Therapy is Reviewed as a Hatha, Classical, Med Yoga (ND) Base. Take practice notes as needed or repeat videos.
* Fused Teaching Vod, 720 Res, Mp4: Yoga Therapy Meets Reiki Review. Take Practice notes as needed or repeat videos.
* Video, 720 Res, Mp4: Practitioner Congrats and Workshop Visual Review with Suggestions.
♥ Bonus Studio Video, 720 Res, Mp4: Our 1st Reiki Video. Produced under Yogi Goddess, LDM Recording. As a Reiki, Kundalini, ASMR Spa, Music Visual. For Our Remastered, Beatz Single for Goddess Vevo Watchers. https://www.reverbnation.com/yogigoddess
* ♥ Our Videos are Vevo TV and promoted within the LDMMIA Profiles.
* Scheduled upload for or by Weekend Friday June 13th.
* LDMMIA Digital & Merch Shop: https://ldm-mia.creator-spring.com
* ♥ As a student, make sure you have high speed connections/wifi for attendance. This sounds basic, I know lol. But, for our video section. The High Speed and Tech is necessary. Otherwise, any device can be used. Our Zip drive files should serve MAC/PC as well.
* ♥ On TECH Emergency: I have had some rare, rough, horrid timed situations as a Remote Student. Pros and Cons to being on campus. So Any Starbucks (coffee shop) or library can be used for wifi hot spots. You can work at your own speed and pace.
* ♥ We will not be hosting deadlines, tests/exams.
* ♥Any homework will be session practice and business planning. Nothing stressful or assignment submissions.
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📘 Key Areas Covered in this Presentation:
Introduction to the concept of community health nursing
Approaches to community health:
Nursing Process Approach
Epidemiological Approach
Evidence-Based Approach
Problem-Solving Approach
Nursing Theories in Community Health Practice
Explanation of teamwork and intersectoral coordination
Concept of primary health care and its application in community nursing
Levels of health care delivery – primary, secondary, and tertiary care
Home visit process: principles, planning, implementation, and follow-up
Use of community bag and record maintenance
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Community Health Officers (CHOs)
Staff Nurses
ASHA workers
Public Health Nurses (PHNs)
Documentation and reporting in community settings
Promotion of health education, immunization, maternal and child health, and nutritional support
Role of nurse in disease surveillance, outbreak control, and health promotion
Ethical principles in community nursing
Coordination with health team members and village health committees
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Nursing students preparing for university theory exams, class tests, or viva
Nursing educators conducting lectures or field discussions
Interns and trainees working in PHCs, sub-centers, or community settings
Community nurses and health educators involved in rural and urban outreach
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✅ The PPT covers the following topics in detail:
Introduction to Nutrition and its role in health and disease prevention
Objectives of nutritional assessment in community settings
Types of nutritional assessment – Anthropometric, Biochemical, Clinical, and Dietary (ABCD) methods
Tools and techniques used in each type of nutritional assessment
Interpreting growth charts, BMI, MUAC, and dietary recalls
Identification of malnutrition, both undernutrition and overnutrition
Common nutritional deficiencies – protein-energy malnutrition, anemia, vitamin A deficiency, iodine deficiency
Principles of nutrition education and behavior change communication
Role of community health nurse in nutrition education during home visits, camps, and school health programs
Use of charts, posters, flashcards, and food models in health teaching
Culturally appropriate and locally available food suggestions
Strategies for promoting infant and young child feeding (IYCF)
National nutrition programs: POSHAN Abhiyaan, Mid-Day Meal Scheme, ICDS, and Anemia Mukt Bharat
Monitoring and evaluation of nutrition interventions
This PPT is perfect for:
B.Sc Nursing students preparing for unit tests or university exams
Nursing educators delivering community health lessons
Field work, community posting presentations, or group health teaching
Health educators and ASHA trainers working on community nutrition
All content is student-friendly, professionally formatted, and aligned with public health priorities and practical nursing roles.
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✳️ Topics Covered in the PPT:
Definition and scope of environmental science and environmental health
Importance of a safe environment for public health
Types of environmental pollution – air, water, soil, noise, and radiation
Sources, effects, and prevention of different types of pollution
Concept of ecosystem and its components
Water safety and purification methods at household and community levels
Disposal of waste and excreta – types, methods, health risks
Introduction to environmental sanitation
Vector control measures: Mosquitoes, houseflies, rodents, etc.
Biological and non-biological health hazards in the environment
National programs related to environmental health and sanitation
Health education for safe water, hygiene, and sanitation behavior change
Role of a community health nurse in promoting environmental health
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Practical methods for solid waste management and waste segregation
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Class lectures and revision
Health teaching in field visits
Community awareness campaigns
Internal assessments and final exam preparation
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Aggregation computation over distributed data streams
- 1. A ggregation C omputation O ver D istributed D ata S treams (partial content) Yueshen Xu Middleware, CCNT Zhejiang University Middleware, CCNT, ZJU 12/15/11
- 2. Paper reference What's Different: Distributed, Continuous Monitoring of Duplicate- Resilient Aggregates on Data Streams Published in ICDE, 2006 Cited by 61 times By Graham Cormode, S. Muthukrishnan etc. 12/15/11 Middleware, CCNT, ZJU I think it’s a good reading suitable for freshmen on distributed data streams Bell Lab Expert/27 Rutgers Expert/45 !
- 3. Background Distributed Data Streams Where and why? Large scale monitoring applications Many sensors distributed over a wide area 12/15/11 Middleware, CCNT, ZJU Just one example Distributed Streaming Model Query paradigm Centralized Decentralized VS
- 4. Constraints and Features Constraints Space Embedded equipments don’t have enough memory Processing power The same reason Communication capability Unreliable, spotty and sporadic 12/15/11 Middleware, CCNT, ZJU All resources are restricted Features Different from ad hoc queries in DBMS, but continuous What’s different?
- 5. Trouble Duplication Why? Wide scale monitoring invariably encounters the same events at different points 12/15/11 Middleware, CCNT, ZJU Instances The same flow will be observed in different routers The same individual will be observed by several mobile sensors Requirement Duplicate-resilient aggregate Two vital questions What is the amount of duplication in the network? What are the versions of classical aggregates in the presence of duplicates? root of all evil
- 6. Topic What kind of topics are researchers interested in ? Aggregation computation Routing algorithms … What is the aggregation? Summarization, namely a statistic variable describing the original data sets Examples min, max, quantile, heavy hitter distinct counts, average, sum … 12/15/11 Middleware, CCNT, ZJU Not strange contacting with data streams Why aggregation? transaction
- 7. Problems and Concerns Distinct count To obtain the number of distinct data (item, record, etc) in multi-sets, namely the cardinality Distinct sample Important, but I’m sorry that I haven’t finished this part 12/15/11 Middleware, CCNT, ZJU What does this paper concern about? Priority: correctness, communication cost Computational cost, space cost ! Features attached to those algorithms applied to distributed environments
- 8. Distinct Counting: Flajolet-Martin Sketch Flajolet-Martin Sketch P. Flajolet, G. Martin . Probabilistic Counting Algorithms for Data Base Applications . Journal of Computer and System Sciences, 1985(Cited by 628) Goal: To estimate the cardinalities of multi-sets of data using relative small space by one pass scan The sketch is a kind of data structure, which is the way to obtain the aggregation results. (skyline) I think this method can be regarded as the classical application of probability without complexity. 12/15/11 Middleware, CCNT, ZJU Give a question: How about you dealing with this problem? The computing paradigm of sketching Be appropriate for using in data streams inherently
- 9. Flajolet-Martin Sketch(Cont.) Preliminary what do we need? the Multi-set M, containing all items/records, and |M| = n the upper bound on the number of distinct items/records U, which is more than n the bitmap B, consisting of L elements, and 2 L = U the hash function h(x: item/record), transforming each items into a binary string distributed uniformly over the range of [1…2 L ], just like b 1 b 2 …b L , in which b 1 is the lowest digit, and b L is the highest the p(x), attaining the left most position of ‘1’ 12/15/11 Middleware, CCNT, ZJU counting not computing 1 1 … 0 B 0 PPT VS Whiteboard ? x record h(x) 1 L
- 10. Flajolet-Martin Sketch(Cont.) The algorithm itself the core task: Remarking the position of which the leftmost ‘1’ of the hash value recorded by p(x) in bitmap B 12/15/11 Middleware, CCNT, ZJU for i:=1 to L do bitmap[i] :=0 for all x in M do begin index := p(hash(x)); if bitmap[index] = the bitmap[index] :=1; end Why?
- 11. Flajolet-Martin Sketch(Cont.) The explanation The fact: bitmap[k] equals to 1 iff after execution a pattern of the form 0 k-1 1 has appeared amongst hashed values of records in M The probability: the occurrence probability of the pattern 0 k-1 1 is 1/2 k Occurrence times: so if |M| = n, then bitmap[1] is accessed approximately n/2 times, bitmap[2] approximately n/4 times Extension: bitmap[k] will almost certainly be zero if k >> log 2 (n) and one if k << log 2 (n) wit a fringe of 0 and 1 for k ≈ log 2 (n) Selection: the leftmost 0, the rightmost 1 or something else 12/15/11 Middleware, CCNT, ZJU U The most practical part is over, and the left is very complicated taken for proving and error analysis, namely all about mathematic for i:=1 to L do bitmap[i] :=0 for all x in M do begin index := p(hash(x)); if bitmap[index] = the bitmap[index] :=1; end
- 12. Flajolet-Martin Sketch(Cont.) Conclusion Analysis Bit-based, reducing the space complexity by constant level Space complexity O(log(n)) O(log(log(n))) Duplicate-insensitive duplicate-resilient and flexible Order-insensitive stable and robust Additivity The ability to merge two FM sketches together, and the merger is simply the bitwise-or of each pair of corresponding bitmaps Questions How to make the value of U? What’s the relationship of U and n? How to make the analysis to the error? … 12/15/11 Middleware, CCNT, ZJU nice qualities for distributed aggregation
- 13. Question What’s the relationship between sketch and skyline? Are they the same? Or… Does the aggregation computation belong to the research fields of data mining ? No, I think 12/15/11 Middleware, CCNT, ZJU
- 14. Q&A 12/15/11 Middleware, CCNT, ZJU