Synopsis:
Mining: Mining:
(Focus) Eunika Mercier-Laurent-The Innovation Biosphere_ Planet and Brains in the Digital Era-Wiley-ISTE (2015).pdf.txt
AI techniques may help in image mining. The alternative solution is to educate society with the aim of preventing crimes. 1. 2. 2. 2. Innovation at home The early development of home automation appeared in France in the 1980s.
Intelligent image mining systems can help to monitor what is happening. The exodus of people looking for jobs from village to town
(Management for Professionals) Jan vom Brocke, Theresa Schmiedel (eds.)-BPM - Driving Innovation in a Digital World-Springer International Publishing (2015).pdf.txt
observations, or more recently process mining. Subsequent activities are dedicated then to identifying process issues and their root causes
and Richard Welch Part III Driving Innovation Through Advanced Process Analytics Extracting Event Data from Databases to Unleash Process Mining...
129 Jan Recker Enabling Process Innovation via Deviance Mining and Predictive Monitoring...145 Marlon Dumas and Fabrizio Maria Maggi Identification of Business Process Models in a Digital World...
for example, meanwhile allow for real-time mining of business processes based on the digital traces that single process steps leave or based on text mining possibilities (Guâ nther, Rinderle-Ma, Reichert, Van der
For example, monitoring and analyzing process performances based on digital processes enables real-time deviance mining, i e. the identification of best and worst process performances (see the chapters by Recker (2015) and by Dumas and Maggi (2015)).
He introduces an approach to create event logs from underlying databases as a fundamental prerequisite for the application of process-mining techniques
Marlon Dumas and Fabrizio Maria Maggi give insights on â€oeenabling Process Innovation via Deviance Mining and Predictive Monitoringâ€.
Enabling process innovation via deviance mining and predictive monitoring. In J. vom Brocke & T. Schmiedel (Eds.
Using process mining to learn from process changes in evolutionary systems. International Journal of Business Process Integration and Management, 3 (1), 61†78.
Many of the required process technologies and methods such as process mining and business analytics have been researched
and methods such as process mining and analytics have been researched and developed extensively (Grigori et al.,2004). ) Even business activity monitoring or complex event processing are available as off the shelves solutions (Luckham, 2011.
Process Innovation with Disruptive Technology in Auto Insurance 101 Part III Driving Innovation Through Advanced Process Analytics Extracting Event Data from Databases to Unleash Process Mining Wil M
. P. van der Aalst Abstract Increasingly organizations are using process mining to understand the way that operational processes are executed.
Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks,
Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However,
binds, and classifies data to create â€oeflat†event logs that can be analyzed using traditional process-mining techniques.
Today, there are many mature process-mining techniques that can be used directly in everyday practice (Aalst, 2011.
Disco (Fluxicon), Perceptive Process Mining (Perceptive Software, before Futura Reflect and BPMONE by Pallas athena), ARIS Process Performance Manager (SOFTWARE AG), Celonis Process Mining (Celonis Gmbh
Despite the abundance of powerful process-mining techniques and success stories in a variety of application domains, 2 a limiting factor is the preparation of event data.
In fact, whenever possible, process-mining techniques use extra information such as the resource (i e.,, person or device) executing
, typically the audit trail provided by the system can directly be used as input for process mining.
Extracting Event Data from Databases to Unleash Process Mining 107 technology often provides so called â€oeredo logs†that can be used to reconstruct the history of database updates.
To understand why process-mining techniques need â€oeflat event logs†(i e. event logs with ordered events that explicitly refer to cases
First, we introduce process mining in a somewhat more detailed form (Sect. 2). Section 3 presents twelve guidelines for logging.
The results serve as input for conventional process-mining techniques. Section 7 discusses related work
and Sect. 8 concludes this paper. 2 Process Mining Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs readily available in today†s information systems (Aalst,
2011). 108 W. M. P. van der Aalst Normally, â€oeflat†event logs serve as the starting point for process mining.
For example, many process-mining techniques use extra information such as the resource (i e.,, person or device) executing
We refer to the XES standard (IEEE Task force on Process Mining 2013b) for more information on the data possibly available in event logs.
Flat event logs such as the one shown in Table 1 can be used to conduct four types of process mining (Aalst, 2011.
Process discovery is the most prominent process-mining technique. For many organizations it is surprising to see that existing techniques are indeed able to discover real processes merely based on example behaviors stored in event logs. •The second type of process mining is conformance.
this third type of process mining aims at changing or extending the a priori model. For instance, by using timestamps in the event log one can extend the model to show bottlenecks, service levels,
either discovered through process mining or (partly) made by hand, one can check, predict, or recommend activities for running cases in an online setting.
Extracting Event Data from Databases to Unleash Process Mining 109 The Prom framework provides an open source process-mining infrastructure.
Over the last decade hundreds of plug-ins have been developed covering the whole process-mining spectrum. Prom is intended for process-mining experts.
Non-experts may have difficulties using the tool due to its extensive functionality. Commercial process-mining tools such as Disco, Perceptive Process Mining, ARIS Process Performance Manager, Celonis Process Mining, QPR Processanalyzer, Fujitsu Interstage
Process Discovery, Stereologic Discovery Analyst, and XMANALYZER are typically easier to use because of their restricted functionality.
Figure 1 shows four screenshots of process-mining tools analyzing the same event Log in this paper,
we neither elaborate on the different process-mining techniques nor do we discuss specific process-mining tools.
Instead, we focus on the event data used for process mining. 3 Guidelines for Logging The focus of this paper is on the input side of process mining:
Such an event log can be used as input for a wealth of process-mining techniques.
The guidelines for logging (GL1€ GL12) aim to create a good starting point for process mining.
d) Celonis process mining (Celonis Gmbh)( Color figure online) Extracting Event Data from Databases to Unleash Process Mining 111 specific extensions (see for example the extension mechanism of XES (IEEE Task force
on Process Mining, 2013b. GL3: References should be stable (e g.,, identifiers should not be reused or rely on the context).
For comparative process mining, it is vital that the same logging principles are used. If for some groups of cases, some events are recorded not
Reproducibility is key for process mining. For example, do not remove a student from the database after he dropped out since this may lead to misleading analysis results.
The main purpose of the guidelines is to point to problems related to the input of process mining.
In fact, for most process-mining projects event data need to be extracted from conventional databases. This is often done in an ad hoc manner.
The event model relates coherent set of changes to the underlying database to events used for process mining.
Extracting Event Data from Databases to Unleash Process Mining 113 Definition 1 (Unconstrained Class Model) Assume V to be some universe of values (strings
there cannot be two concerts on the same day in the same concert hall Fig. 2 Example of a constrained class model (Color figure online) Extracting Event Data from Databases to Unleash Process Mining 115
Extracting Event Data from Databases to Unleash Process Mining 117 Definition 6 (Events) Let CM Â C;
model (Color figure online) Extracting Event Data from Databases to Unleash Process Mining 119 Next we define the effect of an event occurrence, i e.,
Instead, we aim to relate database updates to event logs that can be used for process mining.
and Classify Process-mining techniques require as input a â€oeflat†event log and not a change log as described in Definition 10.
Table 1 shows the kind of input data that process-mining techniques expect. Such a conventional flat event log is a collection of events where each event has the following properties:
Dedicated process-mining formats like XES or MXML allow for the storage of such event data.
To be able to use existing process-mining techniques we need to be able to extract flat event logs
into a collection of conventional events logs that serve as input for existing process-mining techniques.
one may convert it into a conventional event by Extracting Event Data from Databases to Unleash Process Mining 121 taking tsi as timestamp and eni as activity.
and compare the process-mining results. To allow for comparative process mining, process instances are classified using a relation class ï¿
PI ï¿CL with CL the set of classes. Consider for example the study process of students taking a particular course.
In (Aalst, 2013b), the notion of process cubes was proposed to allow for comparative process mining. In a process cube events are organized using different dimensions.
and drill-down process-mining results efficiently. As mentioned before, we deliberately remain at the conceptual level
and classify†approach allows for the transformation of database updates into events populating process cubes that can be used for a variety of process-mining analyses. 7 Related Work The reader is referred to (Aalst, 2011) for an introduction
to process mining. Alternatively, one can consult the Process Mining Manifesto (IEEE Task force on Process Mining, 2011) for best practices and the main challenges in process mining.
Next to the automated discovery of the underlying process based on raw Extracting Event Data from Databases to Unleash Process Mining 123 event data,
there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations,
Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. For example, dozens of process discovery (Aalst, 2011;
However, this paper is not about new process-mining techniques but about getting the event data needed for all of these techniques.
Probably, there are process-mining case-studies using redo/transaction logs from database management systems like Oracle RDBMS, Microsoft SQL SERVER, IBM DB2,
Most closely related seem to be the work on artifact-centric process mining (ACSI, 2013; Fahland, Leoni, Dongen, & Aalst, 2011a;
The 15 case studies listed on the web page of the IEEE Task force on 124 W. M. P. van der Aalst Process Mining (IEEE Task force on Process Mining,
2013a) illustrate the appli-cability of process mining. Process mining can be used to check conformance,
detect bottlenecks, and suggest process improvements. However, the most time-consuming part of process mining is not the actual analysis. Most time is spent on locating,
For process mining, however, it is interesting to know when a record was created, updated, or deleted.
The event model relates changes to the underlying database to events used for process mining.
bind, and classify†approach that creates a collection of event logs that can be used for comparative process mining.
Moreover, we would like to relate this to our work on process cubes (Aalst, 2013b) for comparative process mining.
Process mining: Discovery, conformance and enhancement of business processes. Berlin: Springer. Aalst, W. van der (2013a.
Slicing, dicing, rolling up and drilling down event data for process mining. In M. Song, M. Wynn,
Service mining: Using process mining to discover, check, and improve service behavior. IEEE Transactions on Services Computing, 6 (4), 525†535.
Aalst, W. van der (2014. Data scientist: The engineer of the future. In K. Mertins, F. Benaben, R. Poler,
Extracting Event Data from Databases to Unleash Process Mining 125 Aalst, W. van der, Barthelmess, P.,Ellis, C,
Process mining: A two-step approach to balance between underfitting and overfitting. Software and Systems Modeling, 9 (1), 87†111.
Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16 (9), 1128†1142.
Mining process models from workflow logs. In Sixth International Conference on Extending Database Technology (Lecture Notes in Computer science, Vol. 1377, pp. 469†483.
Genetic process mining: An experimental evaluation. Data mining and Knowledge Discovery, 14 (2), 245†304. Barros, A.,Decker, G.,Dumas, M,
Using minimum description length for process mining. In ACM Symposium on Applied Computing (SAC 2009)( pp. 1451†1455.
Log-based transactional workflow mining. Distributed and Parallel Databases, 25 (3), 193†240. Goedertier, S.,Martens, D.,Vanthienen, J,
IEEE Task force on Process Mining. 2011). ) Process mining manifesto. In BPM Workshops (Lecture Notes in Business Information Processing, Vol. 99.
IEEE Task force on Process Mining. 2013a). ) Process mining case studies. Retrieved from http://www. win. tue. nl/ieeetfpm/doku. php?
process mining case studies IEEE Task force on Process Mining. 2013b). ) XES standard definition. Retrieved from www. xes-standard. org Jagadeesh Chandra Bose,
Wanna improve process mining results? It†s high time we consider data quality issues seriously. In B. Hammer, Z. Zhou, L. Wang,
Extracting Event Data from Databases to Unleash Process Mining 127 Reichert, M, . & Weber, B. 2012).
Process mining: Discovery, conformance and enhancement of business processes. Heidelberg: Springer. vom Brocke, J.,Debortoli, S.,Muâ ller, O,
143 Enabling Process Innovation via Deviance Mining and Predictive Monitoring Marlon Dumas and Fabrizio Maria Maggi Abstract A longstanding challenge in the field of business process management
We present two emerging techniques†deviance mining and predictive moni-toring†that leverage information hidden in business process execution logs
or negative deviance (deviance mining) and by continuously estimating the probability that ongoing process executions may lead to undesirable outcomes (predictive monitoring).
In the following section, we outline the architecture of a monitoring system integrating deviance mining and predictive monitoring.
process system that supports deviance mining and predictive monitoring. The figure highlights that both techniques take as input a log of completed business process execution traces and a set of business constraints.
raising flags whenever certain actions heighten the probability of undesirable deviations. 3 Deviance Mining Business process deviance mining is a family of process mining techniques aimed at analyzing business process execution logs
Business Process Suport System Predictive Monitoring Deviance Mining Business Process Execution Log Business Goals Uncompleted Case Process Worker Process Analyst
Recommendations Diagnostics Fig. 1 Business process support with deviance mining and predictive monitoring Enabling Process Innovation via Deviance Mining and Predictive Monitoring 147 A concrete example of negative deviance mining in a large Australian insurance company has been reported by Suriadi,
Wynn, Ouyang, ter Hofstede, and Van dijk (2013. In this case, a team of analysts sought to find the reasons why certain simple claims that should normally be handled within a few days were taking substantially longer to be resolved.
Another case study showing the potential of deviance mining, this time in the healthcare domain, is reported by Lakshmanan, Rozsnyai, and Wang (2013).
The observations made using sequence mining were complemented with additional observations obtained by comparing a process model discovered from cases with positive outcomes with the model obtained for cases with negative outcomes.
The techniques they employ fall under a wider family of techniques known as discriminative sequence mining techniques (Lo, Cheng, & Lucia
the method analyzes the performance of Enabling Process Innovation via Deviance Mining and Predictive Monitoring 149 process workers to determine which process workers perform better for different types of activities.
Enabling Process Innovation via Deviance Mining and Predictive Monitoring 151 Other approaches focus on generating predictions to reduce risks.
In this setting, we position deviance mining and predictive monitoring as two keystones in modern business process support systems.
However, while deviance mining tries to do this off-line (by analyzing process logs), predictive monitoring provides feedback on the fly-fly to prevent violations.
& 152 M. Dumas and F. M. Maggi Yu, 2008) could find useful applications in the context of both deviance mining and predictive monitoring.
Enabling Process Innovation via Deviance Mining and Predictive Monitoring 153 Lo, D.,Cheng, H.,& Lucia.
Mining explicit rules for software process evaluation. In Proceedings of the international conference on software and system process (ICSSP)( pp. 118†125.
Time prediction based on process mining. Information systems, 36 (2), 450†475. Weidlich, M.,Ziekow, H.,Mendling, J.,Guâ nter, O.,Weske, M,
Mining sequence classifiers for early prediction. In Proceedings of the SIAM international conference on data mining (SDM)( pp. 644†655.
Mining reference process models and their configurations. In R. Meersman, Z. Tari, & P. Herrero (Eds.),
Extracting event data from databases to unleash process mining. In J. Brocke & T. Schmiedel (Eds.
, process mining, also reveal several disadvantages in the light of the (typically) conflicting goals of business process management
His research interests include workflow management, process mining, Petri nets, BPM, process modeling, and process analysis. He published more than 175 journal papers, 17 books,
His research interests are business process mining declarative business process modeling and information systems monitoring. He has published close to 50 journal and conference articles in these fields.
His research activities include business process management, process mining, software development as well as implementation of information systems.
See Database management systems (DBMS) Deployment models, 79 Design principle, 13,78, 98,135, 146,178, 179,182, 183,221 Deviance mining, 11,13, 146†153 Digital age, 4
, 215†226 mining, 13,18, 22, 105†125,147, 249,250, 263,287, 296,302 monitoring, 60,63, 65,83, 146,152 operation, 276, 281†283,285 outcome, 13,272
135 Secondary notation, 186,187, 219†221,223, 225,226 Semantic standardization, 13, 177†189 Sense and respond, 18,22 Sequence mining, 149 Service architecture, 32â€
Driving Innovation Through Advanced Process Analytics Extracting Event Data from Databases to Unleash Process Mining 1 Introduction 2 Process Mining 3 Guidelines for Logging 4 Class
Two Examples 6. 1 Positive Deviance 6. 2 The System That Wasnâ't to Be used 7 Conclusions References Enabling Process Innovation via Deviance Mining
and Predictive Monitoring 1 Introduction 2 Business Process Monitoring Architecture 3 Deviance Mining 4 Predictive Monitoring 5 Discussion and Outlook References Identification
2012 Evaluation_of_Enterprise_Supports_for_Start-Ups_and_Entrepreneurship-Publication.pdf.txt
) 73 54.1%â 16,192, 797 57.8%Metals and Engineering 28 20.7%â 6, 033,163 21.5%Mining, Quarrying and Indigenous Services (Health and Education Services;
A GUIDE TO ECO-INNOVATION FOR SMEs AND BUSINESS COACHES.pdf.txt
and metal value chain (2006) International Council on Mining & Metal www. icmm. com/page/1183/maximising-value-guidance-on-implementing-materials-stewardship-in-the-minerals
Basedoc.scn
G#0v 8461 MINING G#1v 9713 Asteroid mining 0#2#asteroid miner Asteroid mining 0#2#asteroid mining Asteroid mining
G#1v 9318 Mining G#2v 9319 Bauxite mining 0#3#bauxite mining Bauxite mining G#2v 9320 Calcium carbonate mining
0#3#calcium carbonate mining Calcium carbonate mining G#2v 9321 Chromium mining 0#3#chromium mining Chromium mining G#2v 9322 Coal mining
0#3#coal mining Coal mining G#2v 9323 Cobalt mining 0#3#cobalt mining Cobalt mining G#2v 9324 Copper mining
0#3#copper mining Copper mining G#2v 9325 Gold mining 0#3#gold mine Gold mining 0#3#gold mining Gold mining
G#2v 9326 Graphite mining 0#3#graphite mining Graphite mining G#2v 9327 Iron mining 0#3#iron mining Iron mining
G#2v 9328 Lead mining 0#3#lead mining Lead mining G#2v 9329 Lithium mining 0#3#lithium mining Lithium mining
G#2v 9330 Magnesium mining 0#3#magnesium mining Magnesium mining G#2v 9331 Mining 0#3#mining Mining
G#2v 9332 Molybdenum mining 0#3#molybdenum mining Molybdenum mining G#2v 9333 Nickel mining 0#3#nickel mining Nickel mining
G#2v 9334 Non coal mining 0#3#non coal mining Non coal mining G#2v 9335 Oil shale mining 0#3#oil shale mining Oil shale mining
G#2v 9336 Peat mining 0#3#peat mining Peat mining G#2v 9337 Phosphate mining 0#3#phosphate mining Phosphate mining
G#2v 9338 Silver mining 0#3#silver mine Silver mining 0#3#silver mining Silver mining G#2v 9339 Slate industry
0#3#slate industry Slate industry G#2v 9340 Tantalum mining 0#3#tantalum mining Tantalum mining G#2v 9341 Tin mining
0#3#tin mining Tin mining G#2v 9342 Titanium mining 0#3#titanium mining Titanium mining G#2v 9343 Tungsten mining
0#3#tungsten mining Tungsten mining G#2v 9344 Uranium mining 0#3#uranium mining Uranium mining G#2v 9345 Vanadium mining
0#3#vanadium mining Vanadium mining G#2v 9346 Zinc mining 0#3#zinc mining Zinc mining G#1v 9347 Mining companies
G#2v 9348 Adex mining 0#3#adex mining Adex mining G#2v 9349 Aditya birla group 0#3#aditya birla group Aditya birla group
G#2v 9350 African rainbow minerals 0#3#african rainbow minerals African rainbow minerals G#2v 9351 Agnico-eagle mines 0#3#agnico-eagle mines Agnico-eagle mines
G#2v 9352 Alcan 0#3#alcan Alcan G#2v 9353 Alcoa 0#3#alcoa Alcoa
G#2v 9354 Alrosa 0#3#alrosa Alrosa G#2v 9355 Alumina limited 0#3#alumina limited Alumina limited
G#2v 9356 Aluminerie alouette 0#3#aluminerie alouette Aluminerie alouette G#2v 9357 Amcol international 0#3#amcol international Amcol international
G#2v 9358 Americas petrogas 0#3#americas petrogas Americas petrogas G#2v 9359 Anaconda copper 0#3#anaconda copper Anaconda copper
G#2v 9360 Anglo platinum 0#3#anglo platinum Anglo platinum G#2v 9361 Anglogold ashanti 0#3#anglogold ashanti Anglogold ashanti
G#2v 9362 Antofagasta plc 0#3#antofagasta plc Antofagasta plc G#2v 9363 Aricom 0#3#aricom Aricom
G#2v 9364 Asbestos corporation limited 0#3#asbestos corporation Asbestos corporation limited 0#3#asbestos corporation limited Asbestos corporation limited G#2v 9365 Astra mining
0#3#astra mining Astra mining G#2v 9366 Aurubis 0#3#aurubis Aurubis G#2v 9367 Avalon rare metals
0#3#avalon rare metals Avalon rare metals G#2v 9368 Bard ventures company 0#3#bard ventures company Bard ventures company G#2v 9369 Barrick gold
0#3#barrick gold Barrick gold 0#3#barrick gold corporation Barrick gold G#2v 9370 Bema gold 0#3#bema gold Bema gold
G#2v 9371 Bharat aluminium company 0#3#bharat aluminium company Bharat aluminium company G#2v 9372 Bhp billiton 0#3#bhp billiton Bhp billiton
G#2v 9373 Blaafarvevaerket 0#3#blaafarvevaerket Blaafarvevaerket G#2v 9374 Blackfire exploration 0#3#blackfire exploration Blackfire exploration
G#2v 9375 Bougainville copper 0#3#bougainville copper Bougainville copper G#2v 9376 Breakwater resources 0#3#breakwater resources Breakwater resources
G#2v 9377 Bre-x 0#3#bre-x Bre-x G#2v 9378 Cambior 0#3#cambior Cambior
G#2v 9379 Cameco 0#3#cameco Cameco G#2v 9380 Canadian salt company 0#3#canadian salt company Canadian salt company
G#2v 9381 Candente copper 0#3#candente copper Candente copper G#2v 9382 Canico resource 0#3#canico resource Canico resource
G#2v 9383 Cape breton development corporation 0#3#cape breton development corporation Cape breton development corporation G#2v 9384 Carmeuse 0#3#carmeuse Carmeuse
G#2v 9385 Chinalco 0#3#chinalco Chinalco G#2v 9386 Cliffs natural resources inc 0#3#cliffs natural resources inc Cliffs natural resources inc
G#2v 9387 Coal & allied industries 0#3#coal & allied industries Coal & allied industries G#2v 9388 Coal india limited 0#3#coal india limited Coal india limited
G#2v 9389 Codelco 0#3#codelco Codelco G#2v 9390 Companhia vale do rio doce 0#3#companhia vale do rio doce Companhia vale do rio doce
G#2v 9391 Compañía de acero del pacífico 0#3#compañía de acero del pacífico Compañía de acero del pacífico G#2v 9392 Compass minerals 0#3#compass minerals Compass minerals
G#2v 9393 Compass resources 0#3#compass resources Compass resources G#2v 9394 Consolidated zinc 0#3#consolidated zinc Consolidated zinc
G#2v 9395 Cordero mining company 0#3#cordero mining company Cordero mining company G#2v 9396 Crowflight minerals 0#3#crowflight minerals Crowflight minerals
G#2v 9397 Cuprom 0#3#cuprom Cuprom G#2v 9398 Cyprus mines corporation 0#3#cyprus mines corporation Cyprus mines corporation
G#2v 9399 Dampier salt 0#3#dampier salt Dampier salt G#2v 9400 De beers 0#3#de beers De beers
G#2v 9401 Debswana 0#3#debswana Debswana G#2v 9402 Doe run company 0#3#doe run company Doe run company
G#2v 9403 Drummond company 0#3#drummond company Drummond company G#2v 9404 Dundee corporation 0#3#dundee corporation Dundee corporation
G#2v 9405 Echo bay mines limited 0#3#echo bay mines limited Echo bay mines limited G#2v 9406 Eldorado gold 0#3#eldorado gold Eldorado gold
G#2v 9407 Eldorado mining and refining limited 0#3#eldorado mining and refining limited Eldorado mining and refining limited G#2v 9408 Empire zinc company 0#3#empire zinc company Empire zinc company
G#2v 9409 Energy resources of australia 0#3#energy resources of australia Energy resources of australia G#2v 9410 Eramet 0#3#eramet Eramet
G#2v 9411 Evraz 0#3#evraz Evraz G#2v 9412 Exxaro 0#3#exxaro Exxaro
G#2v 9413 Falconbridge ltd 0#3#falconbridge ltd Falconbridge ltd G#2v 9414 Firestone ventures 0#3#firestone ventures Firestone ventures
G#2v 9415 First quantum minerals 0#3#first quantum minerals First quantum minerals G#2v 9416 Fnx mining 0#3#fnx mining Fnx mining
G#2v 9417 Freeport 0#3#freeport Freeport G#2v 9418 Freeport mcmoran 0#3#freeport mcmoran Freeport mcmoran
G#2v 9419 Gécamines 0#3#gécamines Gécamines G#2v 9420 Glamis gold 0#3#glamis gold Glamis gold
G#2v 9421 Gogebic taconite 0#3#gogebic taconite Gogebic taconite G#2v 9422 Gold fields 0#3#gold fields Gold fields
G#2v 9423 Gold reserve inc 0#3#gold reserve inc Gold reserve inc G#2v 9424 Goldcorp 0#3#goldcorp Goldcorp
G#2v 9425 Growmax agri corp 0#3#growmax agri corp Growmax agri corp G#2v 9426 Grupo méxico 0#3#grupo méxico Grupo méxico
G#2v 9427 Harmony gold 0#3#harmony gold Harmony gold G#2v 9428 Harry winston diamond corporation 0#3#harry winston diamond corporation Harry winston diamond corporation
G#2v 9429 Hearst haggin tevis and co 0#3#hearst haggin tevis and co Hearst haggin tevis and co G#2v 9430 Hillsborough resources limited 0#3#hillsborough resources limited Hillsborough resources limited
G#2v 9431 Hochschild mining 0#3#hochschild mining Hochschild mining G#2v 9432 Hollinger mines 0#3#hollinger mines Hollinger mines
G#2v 9433 Imerys 0#3#imerys Imerys G#2v 9434 International coal group 0#3#international coal group International coal group
G#1v 9524 Mining techniques G#2v 9525 Borehole mining 0#3#borehole Borehole mining 0#3#borehole mining Borehole mining
Collective Awareness Platforms for Sustainability and Social Innovation_ An Introduction.pdf.txt
Badii A. 2000)' Online Point-of-Click Web Usability Mining with Popeval-MB, Webeval -AB and the C-Assure Methodology'.
Deloitte_Europe's vision and action plan to foster digital entrepeneurship.pdf.txt
Mining & quarrying Manufacturing Utilities Water management Construction Business services Wholesale trade Retail trade Transportatio
DIGITAL SOCIAL INNOVATION Collective Awareness Platforms for Sustainability and Social Innovation.pdf.txt
Badii A. 2000)' Online Point-of-Click Web Usability Mining with Popeval-MB, Webeval -AB and the C-Assure Methodology'.
DIGITAL SOCIAL INNOVATION The-Open-Book-of-Social-Innovationg.pdf.txt
contemporary issues †from mining regulations to urban migration †and the dissemination of contemporary film and culture
DIGITAL SOCIAL INNOVATIONThe_Process_of_Social_Innovation.pdf.txt
mining new technologies for social applications In all of these, social innovation is likely to be most successful when there is
eco-innovate-sme-guide.pdf.txt
International Council on Mining & Metal www. icmm. com/page/1183/maximising-value-guidance-on
Education - technology and connectedness.pdf.txt
"Social network Analysis and Mining: 1-15 Stoica, E. A a. G Pitic, and A i. Tara,"Crawling-A Solution For Efficient E-Government.
< Back - Next >
Overtext Web Module V3.0 Alpha
Copyright Semantic-Knowledge, 1994-2011
