다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The past decade has witnessed the modern advances of high-throughput technology and rapid growth of research capacity in producing large-scale biological data, both of which were concomitant with an exponential growth of biomedical literature. This wealth of scholarly knowledge is of significant importance for researchers in making scientific discoveries and healthcare professionals in managing health-related matters. However, the acquisition of such information is becoming increasingly difficult due to its large volume and rapid growth. In response, the National Center for Biotechnology Information (NCBI) is continuously making changes to its PubMed Web service for improvement. Meanwhile, different entities have devoted themselves to developing Web tools for helping users quickly and efficiently search and retrieve relevant publications. These practices, together with maturity in the field of text mining, have led to an increase in the number and quality of various Web tools that provide comparable literature search service to PubMed. In this study, we review 28 such tools, highlight their respective innovations, compare them to the PubMed system and one another, and discuss directions for future development. Furthermore, we have built a website dedicated to tracking existing systems and future advances in the field of biomedical literature search. Taken together, our work serves information seekers in choosing tools for their needs and service providers and developers in keeping current in the field. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/search

http://bme2.aut.ac.ir/mhmoradi/RESEARCH%20METHODS%20COURSE/Database-2011-Lu-database_baq036.pdf

PubMed and beyond: a survey of web tools for searching biomedical literature.

Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.

/pdf/in-memory-big-data-management-and-processing-a-survey-1k2b0h2jut.pdf

In-Memory Big Data Management and Processing: A Survey

https://cyberleninka.org/article/n/325149.pdf

Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store.Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

/pdf/asterixdb-a-scalable-open-source-bdms-45xsjf3ec5.pdf

AsterixDB: a scalable, open source BDMS

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. 
Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

AsterixDB: A Scalable, Open Source BDMS

Motivation: The MEDLINE database, consisting of over 19 million publication records, is the primary source of information for biomedicine and health questions. Although the database itself has been growing rapidly, the search paradigm of MEDLINE has remained largely unchanged.

Results: Here, we propose a new system for exploring the entire MEDLINE collection, represented by two unique features: (i) interactive: providing instant feedback to users' query letter by letter, and (ii) fuzzy: allowing approximate search. We develop novel index structures and search algorithms to make such a search model possible. We also develop incremental-update techniques to keep the data up to date.

Availability: Interactive and fuzzy searching algorithms for exploring MEDLINE are implemented in a system called iPubMed, freely accessible over the web at http://ipubmed.ics.uci.edu/ and http://tastier.cs.tsinghua.edu.cn/ipubmed/

Contact:chenli@ics.uci.edu; xhx@ics.uci.edu

/pdf/interactive-and-fuzzy-search-mky7e8wpcu.pdf

Interactive and fuzzy search

Instant search is an emerging information-retrieval paradigm in which a system finds answers to a query instantly while a user types in keywords character-by-character. Fuzzy search further improves user search experiences by finding relevant answers with keywords similar to query keywords. A main computational challenge in this paradigm is the high-speed requirement, i.e., each query needs to be answered within milliseconds to achieve an instant response and a high query throughput. At the same time, we also need good ranking functions that consider the proximity of keywords to compute relevance scores. In this paper, we study how to integrate proximity information into ranking in instant-fuzzy search while achieving efficient time and space complexities. We adapt existing solutions on proximity ranking to instant-fuzzy search. A naive solution is computing all answers then ranking them, but it cannot meet this high-speed requirement on large data sets when there are too many answers, so there are studies of early-termination techniques to efficiently compute relevant answers. To overcome the space and time limitations of these solutions, we propose an approach that focuses on common phrases in the data and queries, assuming records with these phrases are ranked higher. We study how to index these phrases and develop an incremental-computation algorithm for efficiently segmenting a query into phrases and computing relevant answers. We conducted a thorough experimental study on real data sets to show the tradeoffs between time, space, and quality of these solutions.

Efficient instant-fuzzy search with proximity ranking

Instant search is a new search paradigm that shows results as a user types in a query. It has become increasingly popular in recent years due to its simplicity and power. In an instantsearch system, every keystroke from a user triggers a new request to the server. Therefore, its log has a richer content than that of a traditional search system, and previous log analysis research is not applicable to this type of log. In this paper, we study the problem of analyzing the query log of an instant-search system. We propose a classication scheme for user typing behaviors. We also compare the log of an instant-search system and that of a traditional search system on the same data. The results show that on a people directory search system, instant search can typically save 2 seconds per search, reduce the typing eort by showing the results with fewer characters entered, and increase the success rate.

Inci Cetindil

Papers

AsterixDB: a scalable, open source BDMS

AsterixDB: A Scalable, Open Source BDMS

Interactive and fuzzy search

Efficient instant-fuzzy search with proximity ranking

Analysis of Instant Search Query Logs.