
Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing
Weiqing ZHUANG, Morgan C. WANG, Ichiro NAKAMOTO, Ming JIANG
Journal of Systems Science and Information ›› 2021, Vol. 9 ›› Issue (1) : 16-44.
Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing
Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China's e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.
big data analytics / e-commerce / U.S. and China / literature review {{custom_keyword}} /
Table A1 Quantity of literature from databases from the U.S. and China when searching for specific subject terms |
Searching Subject Terms | ||||||
Literature Databases | Electronic Commerce/Electronic Business | e-commerce/E-Business | ||||
Big Data | Big Data | |||||
Big Data Analytics | Big Data Analytics | |||||
U.S. | ||||||
WoS (Core Collection) | 2036/571 | 3/0 | 0/0 | 6225/2237 | 37/3 | 5/0 |
ProQuest | 8192/3496 | 6/2 | 0/0 | 45366/13475 | 36/2 | 5/0 |
EBSCOhost | 18384/10487 | 2/6 | 0/0 | 32245/9222 | 33/2 | 4/0 |
JSTOR | 173/72 | 0/0 | 0/0 | 782/879 | 0/0 | 0/0 |
Ei Engineering Village | 4114/1201 | 6/0 | 0/0 | 12661/4515 | 50/7 | 9/0 |
ACM Digital Library | 1554/195 | 1/0 | 0/0 | 5170/6249 | 21/30 | 1/3 |
China | ||||||
CNKI (All) | 4599/1119 | 17/5 | 0/0 | 13072/2381 | 85/3 | 1/0 |
CNKI (Periodical) | 3027/774 | 15/3 | 0/0 | 8212/1607 | 57/2 | 0/0 |
CNKI (Master's & Doctoral Dissertations) | 1422/296 | 2/2 | 0/0 | 3961/517 | 18/1 | 0/0 |
Notes: The date of the search was May-10-2018; all the searched subject terms are classified by the field of 'title'. |
Table A2 Quantity of literature from databases from the U.S. and China sorted by year when searching for specific subject terms |
Year | Searching Subject Term 'e-commerce' | Searching Subject Terms 'e-commerce' and then 'Big data' | ||||||||
ProQuest | WoS (Core Collection) | CNKI (All) | CNKI (Periodical) | CNKI (Master's & Doctoral Dissertations) | ProQuest | WoS (Core Collection) | CNKI (All) | CNKI (Periodical) | CNKI (Master's & Doctoral Dissertations) | |
2018 | 1171 | 56 | 309 | 288 | 9 | 2 | 2 | 10 | 9 | 0 |
2017 | 3988 | 481 | 1638 | 948 | 465 | 9 | 15 | 23 | 15 | 3 |
2016 | 3698 | 448 | 1645 | 823 | 560 | 3 | 8 | 26 | 21 | 3 |
2015 | 3521 | 451 | 1528 | 807 | 470 | 6 | 9 | 16 | 5 | 9 |
2014 | 2752 | 338 | 1367 | 620 | 439 | 11 | 3 | 9 | 5 | 2 |
2013 | 1667 | 313 | 1073 | 535 | 304 | 3 | 1 | 4 | 2 | – |
2012 | 1299 | 266 | 1018 | 479 | 272 | 3 | – | – | – | – |
2011 | 1253 | 324 | 1026 | 476 | 206 | 1 | – | – | – | – |
2010 | 971 | 328 | 860 | 418 | 193 | 0 | – | – | – | – |
2009 | 746 | 483 | 682 | 385 | 177 | 0 | – | – | – | – |
2008 | 660 | 434 | 714 | 404 | 146 | 0 | – | – | – | – |
2007 | 597 | 310 | 727 | 380 | 128 | 0 | – | – | – | – |
2006 | 648 | 247 | 603 | 350 | 121 | 1 | – | – | – | – |
2005 | 605 | 281 | 550 | 292 | 74 | 0 | – | – | – | – |
2004 | 656 | 235 | 449 | 249 | 47 | 0 | – | – | – | – |
2003 | 763 | 235 | 411 | 221 | 28 | 0 | – | – | – | – |
2002 | 1151 | 252 | 653 | 213 | 32 | 0 | – | – | – | – |
2001 | 2485 | 296 | 668 | 221 | 15 | 0 | – | – | – | – |
2000 | 7737 | 297 | 205 | 85 | 2 | 0 | – | – | – | – |
1999 | 6901 | 111 | 23 | 15 | – | 0 | – | – | – | – |
1998 | 1722 | 22 | 2 | 2 | – | 0 | – | – | – | – |
1997 | 288 | 10 | 0 | 0 | – | 0 | – | – | – | – |
1996 | 82 | 9 | 1 | 1 | – | 0 | – | – | – | – |
1995 | 11 | – | – | – | – | 1 | – | – | – | – |
Notes: The date of the search was May-13-2018; all the searched subject terms are classified by the field of 'title'. |
Figure 1 (a) Literature quantity retrieved with the 'e-commerce' subject term from U.S. and China databases; (b) Literature quantity retrieved for the 'e-commerce' and 'Big Data' subject terms from U.S. and China databases |
Table A3 Quantity of literature when searching for the subject term of 'Business Intelligence Analytics' |
Year | Searching Subject Term 'Business Intelligence Analytics' | |
WoS (Core Collection) | CNKI(Periodical) | |
2018 | 2 | 3 |
2017 | 14 | 3 |
2016 | 13 | 0 |
2015 | 14 | 6 |
2014 | 4 | 2 |
2013 | 3 | 1 |
2012 | 4 | 2 |
2011 | 3 | 3 |
2010 | 3 | 4 |
2009 | 1 | 4 |
2008 | 1 | 1 |
2007 | 0 | 3 |
2006 | 0 | 4 |
2005 | 1 | 1 |
2004 | – | 1 |
2003 | – | 0 |
2002 | – | 1 |
2001 | – | 1 |
Total | 63 | 40 |
Notes: The date of the search was May-13-2018; all the searched subject terms are classified by the field of 'title'. |
Table A4 Summary of papers within the BI&A research framework |
Field | Issue | Resolved Issues | Unresolved & Future Research Issues | Authors | Database Source |
In Theory | Architecture for enhancing BI and real-time business analytics | Extended a traditional BI architecture with S&R system and analytical services to transform business events into performance indicators and intelligent business actions | Develop a service-oriented business intelligence platform | [ | WoS (Core Collection) |
Visual analytics for converging-business-ecosystem intelligence | Propose business ecosystem intelligence in applying visual analytics | Deep knowledge of domain-specific entities, attributes, characteristics, and culture of business-ecosystem intelligence | [ | ||
Video analytics for business intelligence | Various algorithms and techniques in video analytics of business intelligence | Not mentioned | [ | ||
BI&A solutions in storage and computing | Outline a novel design of BIA solutions | Evaluate a full-fledged solution that spans all layers | [ | ||
Analytics-as-a-consumer-service of BI&A | Extend organizational BI&A environment to a wide range of consumers | Reconsider the framework of a BI environment, data quality, consumer-focused analytics environments | [ | ||
Social business intelligence | Present the technical architecture of a prototype tool for social business intelligence (SBI) | IT artifact empirically tested toward facilitating SBI | [ | ||
Business intelligence and data analytics (BI&DA) | Propose a framework for the development of BI&DA techniques applied to the different issues | Cloud computing, Near Real-Time BI, enterprise search, distributed data mining, data stream mining, time series data mining, information security and BI&DA | [ | ||
Deploying BDA in the cloud for BI | Outline the benefits and challenges involved in deploying big data analytics through cloud computing | The privacy and security of BDA deployed through cloud computing for BI | [ | ||
In Application | Business intelligence and big data analytics in evidence-based medicine | The EBM Process & how BI&A support various evidence-based medicine processes | Broadening the practice of evidence-based medicine through the applications of business intelligence and big data analytics. | [ | |
Evaluating business intelligence and analytics effectiveness | Develop a comprehensive BIA effectiveness diagnostic (BIAED) framework | Continue to refine and deploy the BIAED framework to be effective in different geographies and cultures | [ | ||
The implications of big data analytics on business intelligence | BDA of data collected from Chinese social media enhance BI greatly | Seek more insights on the drivers and inhibitors of use of BDA for BI | [ | ||
Literature review of BI&A in small and medium-sized enterprises | Basic research situation of BI&A's components, solutions, application, adoption, implementation and mobile BI&A, cloud BI&A, etc. | The factors that influence adoption and implementation of BI&A for SMEs, cloud-based and mobile-based BI&A solutions for SMEs | [ | ||
Factors influencing business intelligence and analytics usage | Data-related infrastructure capabilities, top management, market, data management challenges, etc. influence BI&A usage | The relation of organizational performance and factors of BI&A usage | [ | ||
Agile practices for data warehousing and business intelligence (DW/BI) projects | Agile values place less emphasis on tools to individuals, but DW/BI is a sociotechnical role of methodological, organizational, and technological issues | Not mentioned | [ | ||
In Theory | Mode of data analysis agent (DAA) in business intelligence | The architecture of DAA, describe the differences between conventional mode and agent mode of data analysis in BI | Not mentioned | [ | |
Introduce the concept of business ecosystem | Employ the concepts of information communion and competitive intelligence and discuss the way of establishing a new business ecosystem | Not mentioned | [ | CNKI (Periodical) | |
BI analysis and report publish system within.NET framework | Refers to a high-level system structure of BI that is mainly in regard to front-analysis within Microsoft.NET framework | Application of the system structure BI that this paper presents | [ | ||
Framework of real-time BI | Introduce the defects of traditional BI system, and put forward a future framework of real-time BI system | Future research and practice in real-time BI | [ | ||
BI search engines | The advantages of BI search engine compared with traditional search engines | Research more BI search engines | [ | ||
Spatial business intelligence | Online multisource data integration and interactive geovisual analytics in spatial business intelligence | Integration of industrial geovisual analysis and social media data in BI | [ | ||
In Application | Analysis of mainstream business intelligence software | BI software divides into ROLAP, which is apt to conduct massive data, and MOLAP that is more likely to analyze real-time data | Such factors of individual demands and features that affect the adoption of BI software | [ | |
Use BI to support decision-making in Insurance | Design and implementation in insurance data analyzing and decision-making based on BI | Business application and value of BI | [ | ||
Application of BI in power dispatching and controlling | Use BI to solve the massive data statistical analysis problem as an example of the D5000 system | The function of data mining of BI should perform a deep research in power systems | [ |
Table A5 Quantity of literature when searching for the relative subject terms of Big Data Models & Algorithms |
Year | Searching Subject Terms | |||||||||||||||||||||||||||||||
T01 | T02 | T03 | T04 | T05 | T06 | T07 | T08 | T09 | T10 | |||||||||||||||||||||||
WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | |||||||||||||
2018 | 33 | 60 | 29 | 23 | 27 | 88 | 40 | 41 | 8 | 34 | 4 | 0 | 1 | 0 | 27 | 9 | 9 | 5 | 5 | 5 | ||||||||||||
2017 | 204 | 154 | 114 | 44 | 285 | 260 | 283 | 104 | 56 | 85 | 8 | 1 | 4 | 3 | 46 | 10 | 29 | 5 | 12 | 10 | ||||||||||||
2016 | 241 | 131 | 111 | 47 | 320 | 303 | 378 | 126 | 65 | 83 | 10 | 5 | 5 | 3 | 42 | 10 | 12 | 4 | 15 | 18 | ||||||||||||
2015 | 127 | 68 | 77 | 31 | 335 | 258 | 414 | 145 | 47 | 100 | 5 | 2 | 1 | 1 | 33 | 10 | 7 | 2 | 6 | 10 | ||||||||||||
2014 | 68 | 57 | 28 | 18 | 244 | 237 | 370 | 128 | 37 | 83 | 3 | 1 | 0 | 1 | 16 | 6 | 2 | 2 | 5 | 4 | ||||||||||||
2013 | 31 | 11 | 15 | 7 | 140 | 149 | 258 | 126 | 39 | 100 | 1 | 6 | 3 | 4 | 0 | |||||||||||||||||
2012 | 4 | 1 | 3 | 2 | 84 | 79 | 175 | 67 | 18 | 87 | 2 | 0 | 0 | |||||||||||||||||||
2011 | 0 | 0 | 1 | 0 | 20 | 46 | 75 | 38 | 22 | 79 | 1 | 2 | ||||||||||||||||||||
2010 | 0 | 1 | 0 | 0 | 18 | 18 | 36 | 16 | 27 | 72 | 2 | |||||||||||||||||||||
2009 | 0 | 0 | 0 | 0 | 12 | 6 | 39 | 3 | 29 | 94 | 0 | |||||||||||||||||||||
2008 | 2 | 0 | 0 | 1 | 1 | 7 | 0 | 20 | 66 | 0 | ||||||||||||||||||||||
2007 | 0 | 1 | 0 | 0 | 3 | 3 | 15 | 63 | 1 | |||||||||||||||||||||||
2006 | 0 | 1 | 0 | 0 | 0 | 18 | 33 | |||||||||||||||||||||||||
2005 | 0 | 0 | 0 | 0 | 0 | 11 | 24 | |||||||||||||||||||||||||
2004 | 0 | 1 | 0 | 0 | 1 | 6 | 16 | |||||||||||||||||||||||||
2003 | 1 | 0 | 0 | 0 | 7 | 19 | ||||||||||||||||||||||||||
2002 | 0 | 0 | 0 | 1 | 4 | 4 | ||||||||||||||||||||||||||
2001 | 0 | 1 | 0 | 2 | 4 | |||||||||||||||||||||||||||
2000 | 0 | 0 | 5 | 1 | ||||||||||||||||||||||||||||
1999 | 0 | 0 | 2 | |||||||||||||||||||||||||||||
1998 | 0 | 1 | 4 | |||||||||||||||||||||||||||||
1997 | 1 | 2 | ||||||||||||||||||||||||||||||
1996 | 1 | 4 | ||||||||||||||||||||||||||||||
1995 | 0 | 3 | ||||||||||||||||||||||||||||||
1994 | 0 | 1 | ||||||||||||||||||||||||||||||
1993 | 0 | 0 | ||||||||||||||||||||||||||||||
1992 | 1 | 0 | ||||||||||||||||||||||||||||||
1965–1991 | 98 | |||||||||||||||||||||||||||||||
Total | 714 | 487 | 379 | 174 | 1485 | 1445 | 2079 | 797 | 550 | 1047 | 30 | 9 | 12 | 8 | 170 | 50 | 59 | 18 | 48 | 52 | ||||||||||||
Year | Searching Subject Terms | |||||||||||||||||||||||||||||||
T11 | T12 | T13 | T14 | T15 | T16 | T17 | T18 | T19 | T20 | T21 | ||||||||||||||||||||||
WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | WoS (Core) | CNKI (Periodical) | |||||||||||
2018 | 36 | 180 | 2 | 1 | 2 | 4 | 9 | 21 | 0 | 0 | 2 | 2 | 1 | 3 | 5 | 5 | 2 | 0 | 6 | 2 | 2 | 5 | ||||||||||
2017 | 153 | 392 | 10 | 5 | 11 | 5 | 41 | 43 | 3 | 1 | 14 | 4 | 3 | 2 | 5 | 8 | 7 | 1 | 26 | 11 | 10 | 15 | ||||||||||
2016 | 188 | 337 | 12 | 6 | 5 | 6 | 65 | 46 | 8 | 3 | 14 | 7 | 6 | 1 | 3 | 5 | 7 | 1 | 26 | 5 | 6 | 9 | ||||||||||
2015 | 165 | 258 | 10 | 10 | 3 | 1 | 31 | 15 | 2 | 1 | 5 | 3 | 3 | 2 | 5 | 2 | 6 | 13 | 3 | 6 | 4 | |||||||||||
2014 | 80 | 169 | 1 | 1 | 5 | 1 | 14 | 12 | 1 | 8 | 0 | 1 | 2 | 1 | 9 | 6 | 3 | 6 | ||||||||||||||
2013 | 38 | 76 | 0 | 3 | 0 | 4 | 1 | 1 | 3 | 1 | 3 | 6 | 2 | 1 | ||||||||||||||||||
2012 | 9 | 20 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | |||||||||||||||||||||||
2011 | 1 | 2 | 0 | |||||||||||||||||||||||||||||
2010 | 0 | 0 | ||||||||||||||||||||||||||||||
2009 | 0 | 0 | ||||||||||||||||||||||||||||||
2008 | 0 | 1 | ||||||||||||||||||||||||||||||
2007 | 0 | |||||||||||||||||||||||||||||||
2006 | 1 | |||||||||||||||||||||||||||||||
Total | 670 | 1435 | 36 | 23 | 29 | 18 | 165 | 139 | 14 | 5 | 45 | 16 | 16 | 8 | 20 | 22 | 27 | 2 | 87 | 29 | 28 | 39 |
Notes: (1) The date of the search was June-11-2018; all of the searched subject terms are classified by the field of 'title'; (2) T01 refers to "Big Data Model"; T02: "Big Data Algorithm"; T03: "Hadoop"; T04: "MapReduce"; T05: "Apriori"; T06: " |
Figure 3 Comparison of the literature from the U.S. and China in terms of the total quantity of big data models and algorithms |
Table A6 Quantity of literature when searching for the subject term of 'E-commerce & Big Data Analytics' |
Year | Searching Subject Terms "E-commerce & Big Data Analytics" | |
WoS (Core Collection) | CNKI (Periodical) | |
2018 | 9 | 0 |
2017 | 24 | 12 |
2016 | 12 | 8 |
2015 | 10 | 6 |
2014 | 2 | 6 |
2013 | 3 | 1 |
2012 | 1 | 1 |
Total | 60 | 34 |
Notes: The date of the search was June-18-2018; all of the searched subject terms are classified by the field of 'title'. |
Table A7 Quantity of literature when searching for the subject term of 'Online Consumer Behavior' |
Year | Searching Subject Term "Online Consumer Behavior" classified by the field of "title" | Searching Subject Terms "Online Consumer Behavior & Big Data" classified by the field of "topic" | ||
WoS (Core Collection) | CNKI (All) | WoS (Core Collection) | CNKI (All) | |
2018 | 9 | 4 | 8 | 0 |
2017 | 26 | 31 | 31 | 1 |
2016 | 34 | 36 | 20 | 1 |
2015 | 23 | 23 | 16 | 0 |
2014 | 15 | 26 | 7 | 0 |
2013 | 13 | 19 | 2 | 0 |
2012 | 12 | 25 | 1 | 0 |
2011 | 19 | 17 | 0 | 0 |
2010 | 9 | 16 | 1 | 0 |
2009 | 12 | 14 | 1 | 0 |
2008 | 8 | 11 | 1 | 0 |
2007 | 5 | 3 | 0 | 0 |
2006 | 8 | 5 | 0 | 0 |
2005 | 5 | 4 | 0 | 0 |
2004 | 2 | 0 | 0 | 0 |
2003 | 4 | 2 | 1 | 0 |
2002 | 6 | 0 | 0 | 0 |
2001 | 1 | 1 | 0 | 0 |
2000 | 4 | 0 | 0 | 0 |
1999 | 0 | 0 | 1 | 0 |
1998 | 1 | 0 | 0 | 0 |
Total | 216 | 238 | 90 | 2 |
Notes: The date of the search was June-21-2018. |
Table A8 Quantity of literature when searching for several relative subject terms of Big Data Analytics in e-commerce |
Year | Searching Subject Term "Internet of Things & e-commerce" | Searching Subject Terms "Mobile Technology & e-commerce" | Searching Subject Term "Cloud Computing & e-commerce" | Searching Subject Terms "Artificial Intelligence & Big Data & e-commerce" | Searching Subject Terms "Quantum Computing" | |||||
WoS (Core Collection) | CNKI (Periodical) | WoS (Core Collection) | CNKI (Periodical) | WoS (Core Collection) | CNKI (Periodical) | WoS (Core Collection) | CNKI (Periodical) | WoS (Core Collection) | CNKI (Periodical) | |
2018 | 10 | 28 | 21 | 3 | 16 | 31 | 3 | 17 | 57 | 10 |
2017 | 26 | 120 | 73 | 12 | 39 | 78 | 6 | 31 | 93 | 26 |
2016 | 23 | 136 | 66 | 17 | 43 | 99 | 11 | 17 | 106 | 19 |
2015 | 16 | 162 | 54 | 23 | 32 | 151 | 0 | 24 | 69 | 16 |
2014 | 5 | 86 | 44 | 19 | 29 | 98 | 0 | 11 | 87 | 19 |
2013 | 6 | 92 | 41 | 17 | 31 | 86 | 3 | 8 | 76 | 5 |
2012 | 3 | 53 | 22 | 10 | 14 | 80 | 4 | 2 | 58 | 7 |
2011 | 5 | 59 | 34 | 11 | 9 | 72 | 3 | 6 | 85 | 13 |
2010 | 0 | 33 | 25 | 9 | 5 | 31 | 0 | 5 | 72 | 9 |
2009 | 1 | 2 | 37 | 11 | 5 | 6 | 0 | 7 | 71 | 18 |
2008 | 3 | 0 | 37 | 14 | 0 | 3 | 0 | 7 | 83 | 11 |
2007 | 1 | 1 | 23 | 14 | 0 | 0 | 5 | 9 | 89 | 13 |
2006 | 2 | 1 | 21 | 10 | 0 | 0 | 8 | 5 | 63 | 10 |
2005 | 2 | 0 | 20 | 10 | 0 | 0 | 4 | 5 | 95 | 9 |
2004 | 1 | 0 | 24 | 7 | 0 | 0 | 0 | 8 | 72 | 13 |
2003 | 2 | 0 | 20 | 6 | 0 | 0 | 5 | 5 | 113 | 14 |
2002 | 4 | 0 | 25 | 6 | 0 | 0 | 8 | 3 | 84 | 9 |
2001 | 2 | 0 | 17 | 8 | 0 | 0 | 3 | 4 | 72 | 6 |
2000 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 4 | 63 | 5 |
1999 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 32 | 3 |
1998 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 41 | 6 |
1997 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 1 |
1996 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 |
1995 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 1 |
1994 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 |
1993 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 0 |
1992 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 |
1991 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 |
1990 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 |
Total | 112 | 773 | 620 | 207 | 223 | 735 | 92 | 181 | 1644 | 243 |
Notes: Except for "Quantum Computing" (the search was performed on July-30-2018 classified by the field of "title"); for others, the date of the search was June-21-2018, and the searching subject terms are classified by the field of "topic". |
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