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Oz search
The document discusses the limitations of traditional keyword search engines for patent search and introduces a new patent search engine called Crony. Crony uses text block search technology rather than keyword search to find relevant patents based on both the content and location of text within documents. This allows for accurate, easy and economic patent searches that produce results within seconds without requiring users to create complex search formulas or legal expertise. Crony also saves resources through its lightweight indexing database and real-time updates.
Oz search
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- 2. eBook, eJournal, Paper, Patent,Judgment Keyword Search Motivation for New Search Engine Need to read through the document to find the passage of interest
- 3. Title in here Title in here •Identical Structure and Algorithm • No differentiated value • Innovative Structure and Algorithm • Totally new search Title in here 신개념 검색 서 비스 Keyword Searches Delayed Indexing Low efficient Resource High Cost Resource Back end Pre/Post processing Limitations of the Keyword Search
- 4. Index DB Structure Architectureof OZ Search Memory based design Resources optimizing Indexing & searching speed Index Structure Search and Index Algorithms Shared index DB Multi-level Hashing Bucket slots Low Cost FnByte sharing Bit type format algorithms Block Sorting Memory Optimizing Word Pool Hash index Expansion for key stroke Typo Correction Auto-completion for every keyword Ranking for key strokeindex Inverted Data structure + OzKsana Instant OzBasic Enter OzDnS Text block Instant Search OzMarker Brand OzAim Big Data in memory Search Engines Applications Products Similarity Analyzer Crony Patent + . . . . . .
- 5. Frequency rate Precision rate NoTrade Off Resource Sharing Minimizing Duplication Shared Index DB Memory based Keyword location mngt OzSearch B+ tree General Index ST + index & keywords Slim Engine Through Shared Index structure,. Saving resource by more than 50% and guaranteeing memory based search for big data Trie Frequency rate Precision rate Trade Off Index Structure of OzSearch
- 6. Memory based IndexDB : Employing Multi level Hash index * How to treat collision and sort Bucket Blocks b~ buckets (prime#2) Data… … … Bucket Blocks buckets (Prime#1) a~ … Sorted data b~ …overflow … … ㄱ~ … ㄴ~ … … … Multi Level Hash Index(conceptual diagram) Sort Blocks Data Set aa~ Data(a) ba~ Data(b) … … 가~ … 나~ … … … Sorted Data Slots Shell Sort Data Hash function Slot Data Sort Sort Data Block creation Data: allocating corresponding block Shell Sort Sort blocks sequential mergingHash Collision 처리 Data Hash Function(prime #1) Bucket allocation if Bucket(n) Overflow prime #2 Hash Function Next level bucket(n) creation
- 7. Sample data set Index k ko kor kore korea korean Keyworddids ko #1, #2 korea #1, #3 korean #4 dids Keyword #1, #2, #3, #4 k #1, #2, #3, #4 ko #1, #3, #4 kor #1, #3, #4 kore #1, #3, #4 korea #4 korean #1 : ko, korea #2 : ko #3 : korea #4 : korean OzSearch일반 전방일치 구조 Index Size Document Volume OzSearch Ordinary Engines 0 10 20 30 40 50 일반구조 OzSearch pointer 수 Did 수 일반구조 OzSearch Index 수 6 6 Keyword 수 6 3 Did 수 18 5 Pointer 수 12 9 연산 부하 중 소 비고 - - • small index DB Decrease OP load speedy search • The bigger the data size is, the more the resource can be saved Resource saving Shared index Structure
- 8. Utilizing Low CostFunctions 1) macro : processing time measurement for every module time delay analysis 2) micro : performance check for every library / function 1) Macro analysis : google performance tool use processing time check(CPU profiler) for every module delayed modules logic improvement or micro analysis Sample data: Wikipedia 2) Micro analysis : atoi() function ex. 1 bil ascii to integer conversion atoi() function: about 30 sec new code: within 0.3 ~ 3 sec
- 9. Memory data reductiontechnology SNS, Internet …… DBMS, File Documents sensor Standard 반입 file Standard Input file Bit divide Inverted File create 01010011 00110011 Byte encoder Column wise Bit grouping Re-position Code Temp encoder Formatter 0101 0011 x 3 acde001 defg002 fghi003 … 1,2,3,… acde001 defg002 fghi003 … Output 0011 0011 1100 1100 …… - Memory (Data type simplifying + Byte sharing + slimed data ST) - Disk I/O(usage frequency Grouping + data reduction) OP Analysis
- 10. 40200 600 0 10000 20000 30000 40000 50000 일반알고리즘 OzSearch 대용량 자료연산 알고리즘 (수배차량 조회 2.5억건/일) Ex(1) Wanted car surveillance CCTV data: 0.25 bil images/day Intentional changes: 1 4, 38, 마머… Require real time search Minimum Comparison 3 digit misrecognition 7C1 + 7C2 + 7C3 = 7 + 21 + 105 = 133 + right recog. 1 time = 134 Algorithm General algorithm OzSearch Algorithm 3000image/s * 134 cases = 402000 tps 3000 images/s * 0.2s/image = 600 tps Proof Recognition failure not counted 1. Word correction algorithm 2. Character comparison algorithm to find similar trade mark Operation Algorithm and inverted file Ex(2) KR trade mark search system About 5 million trademarks invert file creation time Algorithm Current mechanism with datamining OzMarker 3.2 bil*0.00003 sec/case = 26.7 hr 5mil * 0.00003 sec/case = 150s Invert file creation 5 mil * (38 = 8 digit * 3 similar char) = 3.2 bil indiecs 5mil indices
- 11. Inverted file size& capacity comparison 20 50 30 11.7 3.2 5 100 0 20 40 60 80 100 120 예스24 (색인크기/GB) 1쇼핑몰 (색인크기/GB) 문장검색 (time/분) 호가매매 (처리용량/상대값) 경쟁사 비큐리오 31 50 4.5 0 20 40 60 Row Wise Invert File OzSearch Big Data invert size comparison (100만계좌 10억건 주식거래 예제) 소요 공간(GB) 32억 500만 0 100000 200000 300000 400000 기존 색인방식 OzSearch 알고리즘 Index count Comparison (230만 유사상표 색인 자료) index 수 402000 600 0 200000 400000 일반알고리즘 OzSearch Big data operation Algorithm (수배차량 조회 2.5억건/일) tps Index size, Searching Time comparison (BMT results)
- 12. Memory Reduction Example 1)Row wise DB ST ≒ 31GB 2) Basic inverted data ST ≒ 50GB * case/column increases, more storage space required 3) Memory Reduction data ST ≒ 4.5GB * case/column increases, efficiency also increases 31 50 4.5 0 10 20 30 40 50 60 Row Wise C/W index Optimize Index size (GB) 소요 공간 Name SSN ACC # … … … 100만 * (20bytes + 13bytes + 20bytes) = 53MB ACC # Designated Code Mass trx y/n? … … … 10억 * (20bytes + 10bytes + 1bytes) = 31GB ~~~ Name SSN ACC # … … … 100만 * (20bytes + 13bytes + 20bytes) = 53MB Mass trx 0 ACC # …… ACC #ACC # Mass trx 1 ACC # ACC #ACC # …… ACC # Designated Code … … … ~~~ (20bytes) * 10억 = 20GB 10억 * (20bytes + 10bytes) = 30GB Original Data 53MB TRX data ≒ 4.4GB Example In case1 billion trx from 1mil accounts at 10 thousand branches, SSN, account #, Name, Mass trx check indexing (64 bits OS)
- 13. Original Data Memory Reduction: DataStructure and Capacity Column SSD(Offset) Original Name + SSN 1M 33bytes 53MBAccount # 1M 20bytes(64bits hash indexing) Designated # 10,000 10bytes TRX Mass TRX 1B 1bit 125MB TRX 1B 20bits(1M ACC)+14bits(10,000 Designated #) 4.25GB Memory reduction data ST and Size Mass TRX 1B bits 0011010101000…… 110101010001101010 ACC # 1M 20 bytes ….. Designated # 10,000(10 bytes) … ….. TRX data(10B) Abs ACC 20 bits(1M) Abs code 14 bits(10,000) …… Analysis/OP format 53MB 125MB 4.25GB
- 14. Memory Reduction Developed Improving Learningfunc. For analysis Data Type reduction Byte sep/share Super light inverted data Structure Standard user data definition API DISK I/O reduction Data Type reduction Column wise compression Server/Index distributed/pararell processing Essential algorithm for each part I/O Super light index DB structure Map reduce / comm. tech Memory Processing Distribution operation Parallel Processing I/O DISK I/O reduction Reduce Communication between servers Big data analysis (NoSQL type) More than hundred libraries Big Data in Memory Technology 50% resource saving search engine technology Developing Generalized Unstructured mass data processing Query Optimizer Core Technology 1) Search Engine indexing Structure and Algorithm 2) String Management related data structure and algorithm 3) Memory / resource efficiency enhancement library 4) Big Data in Memory related technology (based on Search Engine Technology) Status Quo of BeCurio Technology
- 15. Product Explanation Search Engines (OzSearch) Keyword Search Basic Memory/DISK basedresource sharing keyword search engine More than 50% index size reduction OzParser Integrated phoneme analyzer Just for search engine Instant Search Memory based OzKsana Real time keyword recommendation for each character input Real time customized ranking/indexing based on group Compare with Google instant search OzSniper AND search for each character, phoneme Analyzer Powerful spelling correction Text block Search OzDns Real time web based text block search Super fast and light location data index structure No preprocessing Algorithms Customized Search RERE Real time super fast ranking for each character input Based on keyword chain patent registration technology Typo Correction OzFix resource saving more than 100 times Optimizing accuracy and flexibility Compare with Google Super fast search Algorithm Auto completion and expansion for every keyword Dramatic speed improvement and resource reduction logic Solutions Big Data in Memory OzAiM Memory structure reduction data structure reduction data structure ANSI query (NoSql type) Analysis Real time Group by/Order by for more than 10B data Similar Trade mark Search OzMarker Based on OzFix algorithm Super fast indexing, enhanced accuracy and flexibility 6M trade mark indexing 24 hr : 100 sec Prior Patent Search Crony Avoiding search formula by experts Avoiding existing similarity analysis algorithm 1/10 resource + 10 times faster speed Plagiarism Checker OzSoS Plagiarism checker based on DnS Super fast, high accuracy real time text block similarity analysis BeCurio Products
- 16. An Example ofPatent Search Formula ( (web* or internet* or network*) and (brows*) and (HTML* or HTTP* or XML* or Markup* or javascript*) ) OR (((remote* and naviga*) or (spatial adj10 naviga*) or (arrow and (key adj10 naviga*)) or (directional and (key adj10 naviga*)) or (user adj10 interface and naviga*)) and (brows* or menu) ) OR ((((리모컨 or 리모콘) or (화살* and 키) or (방향 and 키) or (유저 adj10 인터페이스)) and 화면 and 선택) ) OR ( (((web) and (client* or browser*) and ((remote* adj10 control*) or (cursor adj10 navigation) or layout) or (web* or internet* or network*) and (brows* or navigat*) and ((remote* adj10 control*) or (user adj interfac*) or layout*))) ) OR ( (gui or presentation) and engine and (XML* or script* or Java*) ) OR ( (web and application and framework) or (web and application and platform) or (web and rich and internet and application) or (web and ria) or (web and ajax) or (web and asynchronous and javascript and xml) or (widget and web) or (gadget and web) or (rss and web) or (really adj3 simple adj3 syndication adj3 web) or (web and ((smart and client) or (smart and agent))) or (web and downloadable adj10 application) or XAML or XUL or MXML or (interface and element and web) ) OR ( (웹 and *플리케이션 and 프레임*) or (웹 and *플 리케이션 and 플랫폼) or RIA or AJAX or *이젝스 or *이잭스 or 아작스 or 위짓 or 위젯 or widget or 가젯 or 가짓 or gadget or RSS or (웹 and 맞춤형정보배달) or (웹 and 스마트 and 클라이언트) or (웹 and 스마트 and 에이전트) or (웹 and 스마트 and 에이젼트) or (웹 and 다 운* and *플리케이션) or XAML or XUL or MXML ) OR ( (CE or (TV or television) or DTV or (digital adj2 (TV or television))) and (service or (web adj10 service) or (mash adj up adj10 service)) ) OR ( (Opera or Yahoo or Konfabulator or Google or Microsoft or ANT or Mozilla or Netscape or MacroMedia or IBM or HP).AP. )
- 17. Crony: New PatentSearch Keyword search Document Search Technology Instant Search Text block Content & Location Prior Patent Search Text block location search
- 18. CRONY Accurate Easy Economic Speedy Content and itsLocation No missing case No need for any formula Minimum Resource Fast Creation of Indexing DB Real Time DB updating Crony: Expected Values
- 19. Crony System Crony vs.Keyword Search Legacy Patent Search Keyword Search Engine Search formula Keyword index DnS Text block Search Patent DB Keyword + location Auto block separation 분리 Patent DBtext block search index dB Keyword index DB Keyword Search result Content + Location search result User within seconds by ordinary user Days or weeks Only by expert Preprocessing Sentence/Paragraph Hash Fn creation Similarity Analysis Finger Printing Cosθ Time delay Accuracy issue Sizable index DB Tweaked Sentence issue Similarity Analysis # of identical KWD, Density, sequence, etc No Pre-Processing
- 20. Processing role speed Textblock search filter Extracting the target for refined analysis Generic index DB search Within a second Extracting thousands cases Location data TB filter TB automatic separation Identical keywords, Distance analysis Within a seconds Extracting hundreds cases Refined analysis engine Text block analysis - Frequency, distance, sequence - Location calculation Within seconds Min. H/W Assumption: 10 page 3M 8GB MM, more than 2CPU, PC server Feature Min additional job(verification DB creation, etc) short term development and launch Millions of Patent data 1000 Refined analysis engine similarity result Minimize operation load Rough Text block operation Refined Text block Operation Text block operation filter DataQuantity Text block search Filter 1. Innovative structure by location based keyword analysis 2. Super fast and highly accurate similarity checking with 1/10 of resource Speed and Accuracy
- 21. • Similar keyworddetection for every keyword • Easy to use • User registration of similar keywords • Sg/Pl conversion • tense conversion • part of speech conversion Query Expansion 1. Detecting intentional search avoidance 2. Automatic Query expansion for similarity analysis Typo correction Root Keyword Search • Intentional type • correcting multiple words with no spacing • Powerful correction algorithm Other features • Detect change in word sequence • Detect spacing change • Detect partial change in words Sentense St
- 22. Text block Indexing1. No pre/post processing 2. Not Using Finger Printing or other previous indexing method 3. Location data indexing for every keyword Detecting intentional avoidance Target data1 Prior patent detection 2 3 Instant Search Engine and others 4 Current Crony System Coverage 5 Unique functions and differentiated service 1. High speed text block search 2. Words sequence check 1. US Patent 1976~ : 3.8 million samples 2. KR Patent 1. Multiple words typo correction (better than Google) 2. Similar query expansion 3. Root word search 1. Customizable coverage and accuracy control 2. Instant search for meta data 3. Saving function for the content of interest
- 23. Text block chainedsearch By right mouse click Chained Search of Similar Patents
- 24. Crony: unique textblock search Real time web based No preprocessing Crony vs. eTBlast by Virginia Tech OzDnS : eTBlast
- 25. X. 사용자 맞춤검색 조건 정의 Variable Role Effect Disparity Distance b/w keywords Accuracy and number of search results Min Text block size Minimum # of words in text block Control # of search results Keyword weight Frequency and boundary ranking adjustment Max Text block size Maximum # of words in text visual representation Word gap Text block separation Accuracy, # of search results Word order Keyword order accordance Accuracy Variable Controls
- 26. Applicable areas withCrony Search • Patent Search/ Judgment Search/ Plagiarism Checker/ Quotation Search/ eBook Content Search, etc • Smart Contact Center • automated text message feedback for the known questions • Script Search • Jumping to the video frame matching to the script line • Removing repeated questions • By instantly showing the similar questions to the character input • Data Mining Search • Data Mining with Search Interface (Anyone can do mining) • Hyper-Knowledge Product: Sharing Knowledge with No effort
- 27. KM system,EDMS, Document, etc Innovative system Personal search pattern and storing Increase core knowledge sharing Innovative knowledge reference / sharing model Super fast instant search Real time web based text block search Creating value added knowledge network by quality knowledge acquisition and sharing Creating Knowledge eco-system Hyper Knowledge Creation Model i. Knowledge branch creation model ii. core contents chain core content sharing iii. Knowledge eco-system by specialized category
- 28. Hyper Knowledge Current Document/Knowledge managementsystem • Document life cycle management • Document search by keyword • Knowledge registration oriented • Low Document utilization (Too many results) Knowledge branching and sharing system • Increasing knowledge utilization by increasing knowledge sharing • Search and share core knowledge text block • voluntary knowledge sharing • Knowledge search based on text block similarity search KMS EDMS CMS ERP… etc Life Cycle mngt. Keyword search Document based Not able to identify the content of interest automatically Search knowledge and its location Knowledge branch creation Sharing knowledge by specialized categories Individual saving text block of interest Creating high quality knowledge Legacy systems Document mngt. sys Hyper Knowledge System Individual Knowledge branch Shared Knowledge network
- 29. Process of KnowledgeNetwork Creation 공유 node report(1) Key Paragraph-1 + wikipedia/original link Key Paragraph-21 + PCM /original link Key Paragraph-31 + report/ link Input Keyword : knowledge network Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph results attachment wikipedia PCM… DnS TBS Wikipedia KOI TBS PCM TBS DnS Instant search report TBS DnS “knowledge network” Key Paragraph-51 + shared node Link Knowledge i. Individual knowledge search and management ii. Personal core knowledge chain creation iii. Automatic Knowledge network creation by specialized categories Enhancing Knowledge creation 10 core paragraphs 10 core paragraphs 10 core paragraphs Infinite Knowledge Network (10 documents 10 core knowledge) Theoretical knowledge combination 10 * 10 * …… * 10 = (10)10 ≒ ∞ … … … Knowledge sharing 저장 Key Paragraph-21 Key Paragraph-1 Key Paragraph-31 Common Interest
- 30. Core Knowledge SharingMap Title : Knowledge block? Author : BeCurio Research Center sentences…………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ………………………………………. ……………………………………….. Key Paragraph -1 ---------------------------- ----------------------------------------------------------- Key Paragraph -2 ---------------------------- ----------------------------------------------------------- Key Paragraph -3 --------------------------- ----------------------------------------------------------- ------------------------------------ Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공 유 node Link + … report(1) KOI drag …(2) KOI drag …(3) KOI drag Post Docs. Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공 유 node Link + … Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공 유 node Link + … Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공 유 node Link + … Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공유 node Link + … Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공유 node Link + … Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공유 node Link + … Key Paragraph-1 + wikipedia/원문 link Key Paragraph-21 + PCM /원문 link Key Paragraph-31 + 보고서/ link Input Keyword : Samsung Date : 2013. 2. 15 Reference Docs : wikipedia / 15 paragraph 질의어 + 출처 + 주요 문장 + 공유 node Link + … Prior Docs. sharing node table Kwd/TB Search doc name, Text block location ref. freq (Line thickness) post doc name, original Link sharing node table creation sharing node Link 공유 map 생성 i. Extracting Core knowledge from huge documents ii. Sharing by core text block similarity iii. Creating knowledge links by specialized categories Core Knowledge Sharing
- 31. K box Growing Knowledge Eco-System Individualchain Hyper Knowledge Creation Growing knowledge eco-system Knowledge network creation process Individual search knowledge saving share node creation knowledge network creation growing knowledge eco-system Search technology Instant search TBS for text body Chain TBS for knowledge effect dramatically improving knowledge sharing, increasing core knowledge acquisition opportunity and saving time high level knowledge creation Category Key Paragraph-1 Key Paragraph-21 share node KP#12 KP#13 KP#22 KP#23 KP#31 KP#32 KP#311 KP#312 KP#n1 KP#n2 report doc www K. network . . . . . . . . . . . . expansion … … … paper
- 32. Instance Search with3 no’s Keyword + Waiting Search Result or No Result Navigation or re-search Current Search Engine Dramatic Improvement of Speed, Quality and Easy of Use No enter key No Waiting No Zero Result zKeyqword Search result (keyword) no result ? Instant Correction for No Result Typo/No result Keyword input New Search Result provided by each input key stroke
- 33. Purchase Department InstantSearch Stock Management Category for individual purchase person (IP / log in ID) Raw Material information for MEMORY Current Stock information Basic product info., related company info. Instant Search Text block Drag & search Super-fast text block content and location search Based on dramatically improved Search speed, a new algorithm for text block search applied Provide super-fast and customized search for each character input to more than 10,000 departments in S group Supplier (domestic/foreign) In-stock or supply information Character Input “M E M O R Y” Customized Instant Search by Department z Instant Search Recommend relevant item for each character input Attachment, manual, product detail, content, etc Text block Search z Text block chain search
- 34. (non)login Search Cart Purchase Recommending Target books ManagingUser/Group Behavior Pattern Personal Pattern Real time recommendation Recommend Filter Group Pattern Steve Jobs Search - Goods Attributes - MD managing points - Target DB Book Recommendation Service RERE category author Recommendation Accuracy (including MD ) event Search Pattern Recommend Filter Recommend Filter Real time Behavior log CRM/log DB Product property Utilizing input keyword, click data and purchase history 10. 6. am 07: 00 Data propagation presentation… book purchase / iphone4S order Real time pattern am 07: 10 Category/author/event score curve am 08: 30 Real time recommendation MD manual recommendation Learning
- 35. Similar Trademark SearchOzMarker Example KR trade mark search system About 5 million trademarks invert file creation time Algorithm Current mechanism with datamining OzMarker 3.2 bil*0.00003 sec/case = 26.7 hr 5mil * 0.00003 sec/case = 150s Invert file creation 5 mil * (38 = 8 digit * 3 similar char) = 3.2 bil indiecs 5mil indices Current OzMarker Processing mechanism Similar character Indexing Typo correction algorithm Better than Google typo correction correction algorithm Indexing time and and size About 24 hr, 10 GB About 100 sec, 500 MB KR Trademark 5 million data Accuracy Depends on similarity definition definition Similar character, similarly pronounced words algorithm Independent of languages Easy of Use Delay of new trademark registration Registration of new trademark trademark within a few seconds Expandability Applying a new pattern requires requires overall indexing No overall indexing Independent of languages
- 36. Big data inMemory solution, AiM 1. Basic Fn(Ansi Query) 2. User Defined Fn 3. Statistics Fn 4. Other data analysis Fn User Defined Basic Fn + = - > < Data analysis tool Add New pattern High level memory utilization Efficient memory use Super light Search Engine Tech Massive data analysis partSearch engine part AiM Existing Search Engine TechGeneral Solution Meet user requirements such as data analysis speed, analysis tool and statistical methods Big Data Structure and Algorithm Fast and convenient
Editor's Notes
- #10 메모리 활용에 예 오픈소스로 가능? = 일반화에 위배 설혹 가능하더라도? 문제???
- #11 알고리즘 측면 현재 시스템에서 불가능한 것으로 판명 특허청 유사 상표 조회 알고리즘의 중요성(차별화된 구조 기반) 오픈소스로 과연 이런 작업이 가능할 것인가?
- #13 실제 사례 Core 기술 이용의 당위성 오픈소스에 대한 끝없는 질문?
- #14 실제 저희 엔진에 사용되고 있는 예 메모리 축약의 중요성 메모리 기반 Big Data 처리 여건 형성
- #15 개발할 상세 요소 기술 구성도 메모리 강조 : big Data in memory 필요 하지만 사라진다 단위서버당 메모리 활용 처리 용량 = 서버당 처리 용량 수준이상 이라면? 대상 자료
- #27 실제 사례 Core 기술 이용의 당위성 오픈소스에 대한 끝없는 질문?
- #29 어떤점이 다른가? 만들어진 엔진을 사용하지 않는다. 설계도면과 내부 구조를 들여다 보는 것과 같은 infra를 가지고 본 과제를 제안 차별성 할일은 ? 누구나 같다, 정/비정형화
- #35 개인화 맞춤 검색과 일반 검색 비교 일반검색 필요에 따라 batch ordering 된 색인 DB 운영 개인화 맞춤 검색 실시간 ranking, feed back, 검색 이력 관리 1~4 의 실용화 요소 기술이 요구 여러가지 개인화 검색의 접근 방법 중 본 과제의 접근 방법 중심 경량화 검색엔진 기술 활용
- #36 어떤점이 다른가? 만들어진 엔진을 사용하지 않는다. 설계도면과 내부 구조를 들여다 보는 것과 같은 infra를 가지고 본 과제를 제안 차별성 할일은 ? 누구나 같다, 정/비정형화