-
Keim, D.A.: Data Mining mit bloßem Auge (2002)
0.08
0.079621606 = product of:
0.199054 = sum of:
0.008173384 = weight(_text_:a in 1086) [ClassicSimilarity], result of:
0.008173384 = score(doc=1086,freq=2.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.15287387 = fieldWeight in 1086, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.09375 = fieldNorm(doc=1086)
0.19088061 = weight(_text_:91 in 1086) [ClassicSimilarity], result of:
0.19088061 = score(doc=1086,freq=2.0), product of:
0.25837386 = queryWeight, product of:
5.5722036 = idf(docFreq=456, maxDocs=44218)
0.046368346 = queryNorm
0.7387768 = fieldWeight in 1086, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.5722036 = idf(docFreq=456, maxDocs=44218)
0.09375 = fieldNorm(doc=1086)
0.4 = coord(2/5)
- Source
- Spektrum der Wissenschaft. 2002, H.11, S.88-91
- Type
- a
-
Tiefschürfen in Datenbanken (2002)
0.05
0.053081065 = product of:
0.13270266 = sum of:
0.005448922 = weight(_text_:a in 996) [ClassicSimilarity], result of:
0.005448922 = score(doc=996,freq=2.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.10191591 = fieldWeight in 996, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=996)
0.12725374 = weight(_text_:91 in 996) [ClassicSimilarity], result of:
0.12725374 = score(doc=996,freq=2.0), product of:
0.25837386 = queryWeight, product of:
5.5722036 = idf(docFreq=456, maxDocs=44218)
0.046368346 = queryNorm
0.49251786 = fieldWeight in 996, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.5722036 = idf(docFreq=456, maxDocs=44218)
0.0625 = fieldNorm(doc=996)
0.4 = coord(2/5)
- Source
- Spektrum der Wissenschaft. 2002, H.11, S.80-91
- Type
- a
-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
0.05
0.047836047 = product of:
0.11959012 = sum of:
0.009535614 = weight(_text_:a in 4577) [ClassicSimilarity], result of:
0.009535614 = score(doc=4577,freq=2.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.17835285 = fieldWeight in 4577, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.109375 = fieldNorm(doc=4577)
0.1100545 = sum of:
0.022102704 = weight(_text_:information in 4577) [ClassicSimilarity], result of:
0.022102704 = score(doc=4577,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.27153665 = fieldWeight in 4577, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.109375 = fieldNorm(doc=4577)
0.087951794 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
0.087951794 = score(doc=4577,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.5416616 = fieldWeight in 4577, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=4577)
0.4 = coord(2/5)
- Date
- 2. 4.2000 18:01:22
- Type
- a
-
Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
0.03
0.033289738 = product of:
0.08322434 = sum of:
0.00770594 = weight(_text_:a in 1737) [ClassicSimilarity], result of:
0.00770594 = score(doc=1737,freq=4.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.14413087 = fieldWeight in 1737, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=1737)
0.0755184 = sum of:
0.025260232 = weight(_text_:information in 1737) [ClassicSimilarity], result of:
0.025260232 = score(doc=1737,freq=8.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.3103276 = fieldWeight in 1737, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=1737)
0.050258167 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
0.050258167 = score(doc=1737,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.30952093 = fieldWeight in 1737, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=1737)
0.4 = coord(2/5)
- Abstract
- Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it
- Date
- 22.11.1998 18:57:22
- Type
- a
-
Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997)
0.03
0.03128923 = product of:
0.07822308 = sum of:
0.009535614 = weight(_text_:a in 2908) [ClassicSimilarity], result of:
0.009535614 = score(doc=2908,freq=8.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.17835285 = fieldWeight in 2908, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0546875 = fieldNorm(doc=2908)
0.06868747 = sum of:
0.024711575 = weight(_text_:information in 2908) [ClassicSimilarity], result of:
0.024711575 = score(doc=2908,freq=10.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.3035872 = fieldWeight in 2908, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0546875 = fieldNorm(doc=2908)
0.043975897 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
0.043975897 = score(doc=2908,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.2708308 = fieldWeight in 2908, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0546875 = fieldNorm(doc=2908)
0.4 = coord(2/5)
- Abstract
- Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation
- Source
- Information systems. 22(1997) nos.5/6, S.349-385
- Type
- a
-
Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997)
0.03
0.030494144 = product of:
0.07623536 = sum of:
0.013347079 = weight(_text_:a in 1270) [ClassicSimilarity], result of:
0.013347079 = score(doc=1270,freq=12.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.24964198 = fieldWeight in 1270, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=1270)
0.06288828 = sum of:
0.012630116 = weight(_text_:information in 1270) [ClassicSimilarity], result of:
0.012630116 = score(doc=1270,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.1551638 = fieldWeight in 1270, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=1270)
0.050258167 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
0.050258167 = score(doc=1270,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.30952093 = fieldWeight in 1270, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=1270)
0.4 = coord(2/5)
- Abstract
- Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings. Presents a novel method for association generation, that answers all desiderata. The method is different from all existing algorithms and especially suitable to textual databases with binary attributes. Uses subword trees for quick indexing into the required database statistics. Tests the algorithm on the Reuters-22173 database with satisfactory results
- Source
- Information systems. 22(1997) nos.5/6, S.333-347
- Type
- a
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
0.02
0.019754423 = product of:
0.049386058 = sum of:
0.0068111527 = weight(_text_:a in 1605) [ClassicSimilarity], result of:
0.0068111527 = score(doc=1605,freq=8.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.12739488 = fieldWeight in 1605, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=1605)
0.042574905 = sum of:
0.011163551 = weight(_text_:information in 1605) [ClassicSimilarity], result of:
0.011163551 = score(doc=1605,freq=4.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.13714671 = fieldWeight in 1605, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=1605)
0.031411353 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
0.031411353 = score(doc=1605,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.19345059 = fieldWeight in 1605, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1605)
0.4 = coord(2/5)
- Abstract
- Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
- Type
- a
-
Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
0.02
0.019326193 = product of:
0.048315484 = sum of:
0.009010308 = weight(_text_:a in 5011) [ClassicSimilarity], result of:
0.009010308 = score(doc=5011,freq=14.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.1685276 = fieldWeight in 5011, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=5011)
0.039305177 = sum of:
0.007893822 = weight(_text_:information in 5011) [ClassicSimilarity], result of:
0.007893822 = score(doc=5011,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.09697737 = fieldWeight in 5011, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=5011)
0.031411353 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
0.031411353 = score(doc=5011,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.19345059 = fieldWeight in 5011, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=5011)
0.4 = coord(2/5)
- Abstract
- The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.
- Date
- 7. 3.2019 16:32:22
- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411
- Type
- a
-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
0.02
0.018768111 = product of:
0.046920277 = sum of:
0.0076151006 = weight(_text_:a in 668) [ClassicSimilarity], result of:
0.0076151006 = score(doc=668,freq=10.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.14243183 = fieldWeight in 668, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=668)
0.039305177 = sum of:
0.007893822 = weight(_text_:information in 668) [ClassicSimilarity], result of:
0.007893822 = score(doc=668,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.09697737 = fieldWeight in 668, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=668)
0.031411353 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
0.031411353 = score(doc=668,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.19345059 = fieldWeight in 668, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=668)
0.4 = coord(2/5)
- Abstract
- 20th century massification of higher education and research in academia is said to have produced structurally stratified higher education systems in many countries. Most manifestly, the research mission of universities appears to be divisive. Authors have claimed that the Swedish system, while formally unified, has developed into a binary state, and statistics seem to support this conclusion. This article makes use of a comprehensive statistical data source on Swedish higher education institutions to illustrate stratification, and uses literature on Swedish research policy history to contextualize the statistics. Highlighting the opportunities as well as constraints of the data, the article argues that there is great merit in combining statistics with a qualitative analysis when studying the structural characteristics of national higher education systems. Not least the article shows that it is an over-simplification to describe the Swedish system as binary; the stratification is more complex. On basis of the analysis, the article also argues that while global trends certainly influence national developments, higher education systems have country-specific features that may enrich the understanding of how systems evolve and therefore should be analyzed as part of a broader study of the increasingly globalized academic system.
- Date
- 22. 3.2013 19:43:01
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586
- Type
- a
-
Lischka, K.: Spurensuche im Datenwust : Data-Mining-Software fahndet nach kriminellen Mitarbeitern, guten Kunden - und bald vielleicht auch nach Terroristen (2002)
0.01
0.011373868 = product of:
0.02843467 = sum of:
0.0028897275 = weight(_text_:a in 1178) [ClassicSimilarity], result of:
0.0028897275 = score(doc=1178,freq=4.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.054049075 = fieldWeight in 1178, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0234375 = fieldNorm(doc=1178)
0.025544941 = sum of:
0.0066981306 = weight(_text_:information in 1178) [ClassicSimilarity], result of:
0.0066981306 = score(doc=1178,freq=4.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.08228803 = fieldWeight in 1178, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0234375 = fieldNorm(doc=1178)
0.018846812 = weight(_text_:22 in 1178) [ClassicSimilarity], result of:
0.018846812 = score(doc=1178,freq=2.0), product of:
0.16237405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046368346 = queryNorm
0.116070345 = fieldWeight in 1178, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0234375 = fieldNorm(doc=1178)
0.4 = coord(2/5)
- Content
- "Ob man als Terrorist einen Anschlag gegen die Vereinigten Staaten plant, als Kassierer Scheine aus der Kasse unterschlägt oder für bestimmte Produkte besonders gerne Geld ausgibt - einen Unterschied macht Data-Mining-Software da nicht. Solche Programme analysieren riesige Daten- mengen und fällen statistische Urteile. Mit diesen Methoden wollen nun die For- scher des "Information Awaren in den Vereinigten Staaten Spuren von Terroristen in den Datenbanken von Behörden und privaten Unternehmen wie Kreditkartenfirmen finden. 200 Millionen Dollar umfasst der Jahresetat für die verschiedenen Forschungsprojekte. Dass solche Software in der Praxis funktioniert, zeigen die steigenden Umsätze der Anbieter so genannter Customer-Relationship-Management-Software. Im vergangenen Jahr ist das Potenzial für analytische CRM-Anwendungen laut dem Marktforschungsinstitut IDC weltweit um 22 Prozent gewachsen, bis zum Jahr 2006 soll es in Deutschland mit einem jährlichen Plus von 14,1 Prozent so weitergehen. Und das trotz schwacher Konjunktur - oder gerade deswegen. Denn ähnlich wie Data-Mining der USRegierung helfen soll, Terroristen zu finden, entscheiden CRM-Programme heute, welche Kunden für eine Firma profitabel sind. Und welche es künftig sein werden, wie Manuela Schnaubelt, Sprecherin des CRM-Anbieters SAP, beschreibt: "Die Kundenbewertung ist ein zentraler Bestandteil des analytischen CRM. Sie ermöglicht es Unternehmen, sich auf die für sie wichtigen und richtigen Kunden zu fokussieren. Darüber hinaus können Firmen mit speziellen Scoring- Verfahren ermitteln, welche Kunden langfristig in welchem Maße zum Unternehmenserfolg beitragen." Die Folgen der Bewertungen sind für die Betroffenen nicht immer positiv: Attraktive Kunden profitieren von individuellen Sonderangeboten und besonderer Zuwendung. Andere hängen vielleicht so lauge in der Warteschleife des Telefonservice, bis die profitableren Kunden abgearbeitet sind. So könnte eine praktische Umsetzung dessen aussehen, was SAP-Spreche-rin Schnaubelt abstrakt beschreibt: "In vielen Unternehmen wird Kundenbewertung mit der klassischen ABC-Analyse durchgeführt, bei der Kunden anhand von Daten wie dem Umsatz kategorisiert werden. A-Kunden als besonders wichtige Kunden werden anders betreut als C-Kunden." Noch näher am geplanten Einsatz von Data-Mining zur Terroristenjagd ist eine Anwendung, die heute viele Firmen erfolgreich nutzen: Sie spüren betrügende Mitarbeiter auf. Werner Sülzer vom großen CRM-Anbieter NCR Teradata beschreibt die Möglichkeiten so: "Heute hinterlässt praktisch jeder Täter - ob Mitarbeiter, Kunde oder Lieferant - Datenspuren bei seinen wirtschaftskriminellen Handlungen. Es muss vorrangig darum gehen, einzelne Spuren zu Handlungsmustern und Täterprofilen zu verdichten. Das gelingt mittels zentraler Datenlager und hoch entwickelter Such- und Analyseinstrumente." Von konkreten Erfolgen sprich: Entlas-sungen krimineller Mitarbeiter-nach Einsatz solcher Programme erzählen Unternehmen nicht gerne. Matthias Wilke von der "Beratungsstelle für Technologiefolgen und Qualifizierung" (BTQ) der Gewerkschaft Verdi weiß von einem Fall 'aus der Schweiz. Dort setzt die Handelskette "Pick Pay" das Programm "Lord Lose Prevention" ein. Zwei Monate nach Einfüh-rung seien Unterschlagungen im Wert von etwa 200 000 Franken ermittelt worden. Das kostete mehr als 50 verdächtige Kassiererinnen und Kassierer den Job.
Jede Kasse schickt die Daten zu Stornos, Rückgaben, Korrekturen und dergleichen an eine zentrale Datenbank. Aus den Informationen errechnet das Programm Kassiererprofile. Wessen Arbeit stark Durchschnitt abweicht, macht sich verdächtig. Die Kriterien" legen im Einzelnen die Revisionsabteilungen fest, doch generell gilt: "Bei Auffälligkeiten wie überdurchschnittlichvielenStornierungen, Off nen der Kassenschublade ohne Verkauf nach einem Storno oder Warenrücknahmen ohne Kassenbon, können die Vorgänge nachträglich einzelnen Personen zugeordnet werden", sagt Rene Schiller, Marketing-Chef des Lord-Herstellers Logware. Ein Kündigungsgrund ist eine solche Datensammlung vor Gericht nicht. Doch auf der Basis können Unternehmen gezielt Detektive einsetzen. Oder sie konfrontieren die Mitarbeiter mit dem Material; woraufhin Schuldige meist gestehen. Wilke sieht Programme wie Lord kritisch:"Jeder, der in dem Raster auffällt, kann ein potenzieller Betrüger oder Dieb sein und verdient besondere Beobachtung." Dabei könne man vom Standard abweichen, weil man unausgeschlafen und deshalb unkonzentriert sei. Hier tut sich für Wilke die Gefahr technisierter Leistungskontrolle auf. "Es ist ja nicht schwierig, mit den Programmen zu berechnen, wie lange beispielsweise das Kassieren eines Samstagseinkaufs durchschnittlich dauert." Die Betriebsräte - ihre Zustimmung ist beim Einsatz technischer Kon trolleinrichtungen nötig - verurteilen die wertende Software weniger eindeutig. Im Gegenteil: Bei Kaufhof und Edeka haben sie dem Einsatz zugestimmt. Denn: "Die wollen ja nicht, dass ganze Abteilungen wegen Inventurverlusten oder dergleichen unter Generalverdacht fallen", erklärt Gewerkschaftler Wilke: "Angesichts der Leistungen kommerzieller Data-Mining-Programme verblüfft es, dass in den Vereinigten Staaten das "Information Awareness Office" noch drei Jahre für Forschung und Erprobung der eigenen Programme veranschlagt. 2005 sollen frühe Prototypen zur Terroristensuche einesgetz werden. Doch schon jetzt regt sich Protest. Datenschützer wie Marc Botenberg vom Informationszentrum für Daten schutz sprechen vom "ehrgeizigsten öffentlichen Überwachungssystem, das je vorgeschlagen wurde". Sie warnen besonders davor, Daten aus der Internetnutzung und private Mails auszuwerten. Das Verteidigungsministerium rudert zurück. Man denke nicht daran, über die Software im Inland aktiv zu werden. "Das werden die Geheimdienste, die Spionageabwehr und die Strafverfolger tun", sagt Unterstaatssekretär Edward Aldridge. Man werde während der Entwicklung und der Tests mit konstruierten und einigen - aus Sicht der Datenschützer unbedenklichen - realen Informationen arbeiten. Zu denken gibt jedoch Aldriges Antwort auf die Frage, warum so viel Geld für die Entwicklung von Übersetzungssoftware eingeplant ist: Damit man Datenbanken in anderen Sprachen nutzen könne - sofern man auf sie rechtmäßigen Zugriff bekommt."
- Type
- a
-
Bath, P.A.: Data mining in health and medical information (2003)
0.01
0.010007498 = product of:
0.025018744 = sum of:
0.010897844 = weight(_text_:a in 4263) [ClassicSimilarity], result of:
0.010897844 = score(doc=4263,freq=8.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.20383182 = fieldWeight in 4263, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=4263)
0.014120899 = product of:
0.028241798 = sum of:
0.028241798 = weight(_text_:information in 4263) [ClassicSimilarity], result of:
0.028241798 = score(doc=4263,freq=10.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.3469568 = fieldWeight in 4263, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=4263)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Data mining (DM) is part of a process by which information can be extracted from data or databases and used to inform decision making in a variety of contexts (Benoit, 2002; Michalski, Bratka & Kubat, 1997). DM includes a range of tools and methods for extractiog information; their use in the commercial sector for knowledge extraction and discovery has been one of the main driving forces in their development (Adriaans & Zantinge, 1996; Benoit, 2002). DM has been developed and applied in numerous areas. This review describes its use in analyzing health and medical information.
- Source
- Annual review of information science and technology. 38(2004), S.331-370
- Type
- a
-
Cardie, C.: Empirical methods in information extraction (1997)
0.01
0.008827171 = product of:
0.022067927 = sum of:
0.009437811 = weight(_text_:a in 3246) [ClassicSimilarity], result of:
0.009437811 = score(doc=3246,freq=6.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.17652355 = fieldWeight in 3246, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=3246)
0.012630116 = product of:
0.025260232 = sum of:
0.025260232 = weight(_text_:information in 3246) [ClassicSimilarity], result of:
0.025260232 = score(doc=3246,freq=8.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.3103276 = fieldWeight in 3246, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=3246)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Surveys the use of empirical, machine-learning methods for information extraction. Presents a generic architecture for information extraction systems and surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture
- Footnote
- Contribution to a special section reviewing recent research in empirical methods in speech recognition, syntactic parsing, semantic processing, information extraction and machine translation
- Type
- a
-
Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005)
0.01
0.008627858 = product of:
0.021569645 = sum of:
0.008173384 = weight(_text_:a in 4655) [ClassicSimilarity], result of:
0.008173384 = score(doc=4655,freq=2.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.15287387 = fieldWeight in 4655, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.09375 = fieldNorm(doc=4655)
0.013396261 = product of:
0.026792523 = sum of:
0.026792523 = weight(_text_:information in 4655) [ClassicSimilarity], result of:
0.026792523 = score(doc=4655,freq=4.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.3291521 = fieldWeight in 4655, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.09375 = fieldNorm(doc=4655)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Source
- Journal of information science. 30(2005) no.6, S.550-
- Type
- a
-
Perugini, S.; Ramakrishnan, N.: Mining Web functional dependencies for flexible information access (2007)
0.01
0.008412599 = product of:
0.021031497 = sum of:
0.01155891 = weight(_text_:a in 602) [ClassicSimilarity], result of:
0.01155891 = score(doc=602,freq=16.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.2161963 = fieldWeight in 602, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=602)
0.009472587 = product of:
0.018945174 = sum of:
0.018945174 = weight(_text_:information in 602) [ClassicSimilarity], result of:
0.018945174 = score(doc=602,freq=8.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.23274569 = fieldWeight in 602, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=602)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- We present an approach to enhancing information access through Web structure mining in contrast to traditional approaches involving usage mining. Specifically, we mine the hardwired hierarchical hyperlink structure of Web sites to identify patterns of term-term co-occurrences we call Web functional dependencies (FDs). Intuitively, a Web FD x -> y declares that all paths through a site involving a hyperlink labeled x also contain a hyperlink labeled y. The complete set of FDs satisfied by a site help characterize (flexible and expressive) interaction paradigms supported by a site, where a paradigm is the set of explorable sequences therein. We describe algorithms for mining FDs and results from mining several hierarchical Web sites and present several interface designs that can exploit such FDs to provide compelling user experiences.
- Footnote
- Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1805-1819
- Type
- a
-
Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001)
0.01
0.008234787 = product of:
0.020586967 = sum of:
0.009535614 = weight(_text_:a in 3835) [ClassicSimilarity], result of:
0.009535614 = score(doc=3835,freq=2.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.17835285 = fieldWeight in 3835, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.109375 = fieldNorm(doc=3835)
0.011051352 = product of:
0.022102704 = sum of:
0.022102704 = weight(_text_:information in 3835) [ClassicSimilarity], result of:
0.022102704 = score(doc=3835,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.27153665 = fieldWeight in 3835, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.109375 = fieldNorm(doc=3835)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Type
- a
-
Blake, C.: Text mining (2011)
0.01
0.008234787 = product of:
0.020586967 = sum of:
0.009535614 = weight(_text_:a in 1599) [ClassicSimilarity], result of:
0.009535614 = score(doc=1599,freq=2.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.17835285 = fieldWeight in 1599, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.109375 = fieldNorm(doc=1599)
0.011051352 = product of:
0.022102704 = sum of:
0.022102704 = weight(_text_:information in 1599) [ClassicSimilarity], result of:
0.022102704 = score(doc=1599,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.27153665 = fieldWeight in 1599, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.109375 = fieldNorm(doc=1599)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Source
- Annual review of information science and technology. 45(2011) no.1, S.121-155
- Type
- a
-
Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007)
0.01
0.008111114 = product of:
0.020277783 = sum of:
0.0067426977 = weight(_text_:a in 600) [ClassicSimilarity], result of:
0.0067426977 = score(doc=600,freq=4.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.12611452 = fieldWeight in 600, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0546875 = fieldNorm(doc=600)
0.013535086 = product of:
0.027070172 = sum of:
0.027070172 = weight(_text_:information in 600) [ClassicSimilarity], result of:
0.027070172 = score(doc=600,freq=12.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.3325631 = fieldWeight in 600, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0546875 = fieldNorm(doc=600)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- The amount of information on the Web has been expanding at an enormous pace. There are a variety of Web documents in different genres, such as news, reports, reviews. Traditionally, the information displayed on Web sites has been static. Recently, there are many Web sites offering content that is dynamically generated and frequently updated. It is also common for Web sites to contain information in different languages since many countries adopt more than one language. Moreover, content may exist in multimedia formats including text, images, video, and audio.
- Footnote
- Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"
- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1791-1792
- Type
- a
-
Wu, T.; Pottenger, W.M.: ¬A semi-supervised active learning algorithm for information extraction from textual data (2005)
0.01
0.00795386 = product of:
0.01988465 = sum of:
0.01021673 = weight(_text_:a in 3237) [ClassicSimilarity], result of:
0.01021673 = score(doc=3237,freq=18.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.19109234 = fieldWeight in 3237, product of:
4.2426405 = tf(freq=18.0), with freq of:
18.0 = termFreq=18.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=3237)
0.009667919 = product of:
0.019335838 = sum of:
0.019335838 = weight(_text_:information in 3237) [ClassicSimilarity], result of:
0.019335838 = score(doc=3237,freq=12.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.23754507 = fieldWeight in 3237, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=3237)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- In this article we present a semi-supervised active learning algorithm for pattern discovery in information extraction from textual data. The patterns are reduced regular expressions composed of various characteristics of features useful in information extraction. Our major contribution is a semi-supervised learning algorithm that extracts information from a set of examples labeled as relevant or irrelevant to a given attribute. The approach is semi-supervised because it does not require precise labeling of the exact location of features in the training data. This significantly reduces the effort needed to develop a training set. An active learning algorithm is used to assist the semi-supervised learning algorithm to further reduce the training set development effort. The active learning algorithm is seeded with a Single positive example of a given attribute. The context of the seed is used to automatically identify candidates for additional positive examples of the given attribute. Candidate examples are manually pruned during the active learning phase, and our semi-supervised learning algorithm automatically discovers reduced regular expressions for each attribute. We have successfully applied this learning technique in the extraction of textual features from police incident reports, university crime reports, and patents. The performance of our algorithm compares favorably with competitive extraction systems being used in criminal justice information systems.
- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.258-271
- Type
- a
-
Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018)
0.01
0.007848554 = product of:
0.019621385 = sum of:
0.012923255 = weight(_text_:a in 4286) [ClassicSimilarity], result of:
0.012923255 = score(doc=4286,freq=20.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.24171482 = fieldWeight in 4286, product of:
4.472136 = tf(freq=20.0), with freq of:
20.0 = termFreq=20.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=4286)
0.0066981306 = product of:
0.013396261 = sum of:
0.013396261 = weight(_text_:information in 4286) [ClassicSimilarity], result of:
0.013396261 = score(doc=4286,freq=4.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.16457605 = fieldWeight in 4286, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=4286)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.
- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.7, S.879-889
- Type
- a
-
Gaizauskas, R.; Wilks, Y.: Information extraction : beyond document retrieval (1998)
0.01
0.0077931583 = product of:
0.019482896 = sum of:
0.0100103095 = weight(_text_:a in 4716) [ClassicSimilarity], result of:
0.0100103095 = score(doc=4716,freq=12.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.18723148 = fieldWeight in 4716, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=4716)
0.009472587 = product of:
0.018945174 = sum of:
0.018945174 = weight(_text_:information in 4716) [ClassicSimilarity], result of:
0.018945174 = score(doc=4716,freq=8.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.23274569 = fieldWeight in 4716, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=4716)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- In this paper we give a synoptic view of the growth of the text processing technology of informatione xtraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining
- Type
- a
