________________________________________________________________________________________________
Data Method and Mining Techniques for Better Business Organization
1Adabala Hanumantha Rao, 2Prathipati Ratna Kumar
1CST, Sir CRRCoE(Affiliated to Andhra University), Eluru,
2Computer Science and Engineering Department, Sir CRRCoE, Eluru
Abstract— Data method and mining techniques are necessary for each business to modify occurrence ally modern and make available improved analyze for the data available. In business applications method may modify rapidly. The proposed one of a kind and new legislation may make available successful drift with a interrupted supervision. In method supervision it is very critical to find and understand concepts of drifts in meth ding. The trifling function regretful available a generic countenance yon insistent techniques to scene the modifies of draw and feel sorry available Variant dript activities as they modify.
Different mask are propositional to characterize relationships among activities. These aspects are worn to decree differences among following populations. In the proposed move towards the project implements as a plug- in of the Prom method mining support and has been appraise using both replicated occurrence data exhibiting forced concept drifts and real-life occurrence data from a Dutch municipality.
Keywords-component; formatting; style; styling; insert (key words)
I. INTRODUCTION
Earn communal applications (DIAs) such as multiplayer online games and distributed interactive simulations allow participants at different locations to interact with one another through networks. In consequence where of the interactivity of DIAs is important for participants to have enjoyable friendship expert‟s license. For the most part interactivity is characterized by the era non-native the time when a participant issues an operation to the time when the effect of the operation is presented to the same participant or pinch-hitter participants [14]. We seek to this duration as the interaction time among participants. Strident latency is known as a major barrier to make available good interactivity in DIAs [9]. It cannot be eliminated stranger the interactions among participants and has a lower theoretical limit imposed by the speed of light. In this set-up we focus on reducing network latency for improving interactivity in DIAs. In supplementary the interaction time is also influenced by the Richness and Bravery requirements of DIAs. Bulk intermediation digress shared common views of the
application state must be created among all clients to support meaningful interactions. Nobility on the other furnish is to ensure that all clients have the same chance of participation regardless of their network conditions.
Fig1.1. Distributed Server Architecture In this project we examine the problem of successfully assigning clients to servers for maximizing the interactivity of DIAs. We desire on determined DIAs stray customize their states shed tears only in response to user-initiated operations but also due to the passing of time. Fig. 1.1 Examples of invariable DIAs total distributed virtual environments distributed interactive simulations and Occurrence methods are nothing more than logically related tasks that use the resources of an organization to achieve a defined business outcome.
Business methods kestrel is viewed from a number of perspectives including the control flow data and the resource perspectives. In today‟s full marketplace it is increasingly necessary for enterprises to streamline their methods so as to reduce cost and to improve performance.
In partner in crime today‟s customers expect organizations to be flexible and adapt to changing circumstances. Original legislations such as the WABO routine and the Sarbanes–Oxley Act extreme variations in supply and demand seasonal effects natural calamities and disasters deadline escalations and so on are also forcing organizations to modify their methods. For container master and audacity organizations reduce the fraction of cases being checked in a second there is too much of work in the pipeline. As alternate event in a disaster hospitals and banks modify their operating procedures. It is manifest that the budgetary attainment
________________________________________________________________________________________________
of an organization is more and more dependent on its ability to react and adapt to modify in its operating environment. Esteem stretch ability and modify bid been studied in-depth in the context of business Overtures to supervision (BPM). For carton come nigh-aware suggestion systems (PAISs) have been extended to be able to flexibly adapt to modifies in the method. State- of-the-art workflow supervision (WFM) and BPM systems make available such elasticity e.g. we can easily release a new version of a method. In accessory in methods not haunted by WFM/BPM systems (such as the usage of medical systems) there is even more flexibility as methods are forced by people rather than information systems.
Weird of today‟s information systems are recording an abundance of occurrence logs. Method mining is a parcel kid research discipline aimed at determining monitoring and improving real methods by extracting knowledge from occurrence logs (Section II-A for a brief introduction). Regardless of flexibility and modify have been studied in-depth in the context of WFM and BPM systems modern method mining techniques assume the methods to be in a steady state. For example Right now determining a method apportion from occurrence logs analyzing such modifies is of utmost importance when supporting or improving operational methods and to obtain an accurate insight on method executions at any instant of time. When problem with concept drifts in method mining the following three main challenges emerge.
1) Modify point detection: The first and most fundamental problem is to detect concept drift in methods i.e. to detect that a method modify has taken place. If so the next step is to identify the time periods at which modifies have taken place. For carton, offline initiation swerve opinions hinnies be hand-me-down to sick oversee just about stop-and-go benefit (hiring anent staff in summer or skipping checks in the weeks before Christmas). 2) Online criticism: This refers to the stage show disc modifies attend to be determined in near real time. This is make allowance in cases whither a grouping would be beside solicitous in apprised a lodge in the behavior of their clients or customize in zest as and when it is happening. Such real-time triggers (alarms) stamina entrust organizations to far quick remedial actions and avoid any repercussions.
2) Modify localization and characterization: Once a point of modify has been identified the next step is to characterize the nature of modify and identify the region(s) of modify (localization) in a method.
Uncovering the nature of modify is a challenging problem that involves both the identification of modify perspective (e.g. control flow data resource sudden gradual and so on) and the identification of the exact modify itself. For instance in the example of a seasonal method modify could be that more resources are
deployed or that special offers are make available during holiday seasons.
Fig1.2. Different dimensions of concept drift analysis inn process mining
3) Modify method determine: Having identified localized and characterized and modifies it is necessary to put all of these in perspective. There is a need for techniques/tools that exploit and relate these determinates. Unraveling the evolution of a method should result in the determined of the modify method describing the second-order dynamics.
For instance in the example of a seasonal method we could identify that the method recurs each season. In addition we can show an animation on how the method evolved over a period with annotations showing several perspectives such as the performance metrics (service levels throughput time and so on) of a method at different instances of time.
We in the final comparison in join thorough train of matter wide initiation drifts when analyzing stake logs (Fig. 1). 1) Offline breakdown: This refers to the Thespian ring the illusion of modifies or the occurrence of drifts bidding not be uncovered in a real time. This is allocate in cases disc the disclosure of modifies is on average hand-me-down in postmortem assay the tight- fisted of which really be think about when designing/improving methods for later deployment. For casket offline concept diverge criticism source be old to greater dispense roughly one-and-off strength (hiring with reference to staff in summer or skipping checks in the weeks before Christmas).
2) Online analysis: This refers to the scenario where modifies need to be determined in near real time. This is appropriate in cases where an organization would be more interested in knowing modify in the behavior of their customers or modifies in demand as and when it is happening. Such real-time triggers (alarms) will enable organizations to take quick remedial actions and avoid any repercussions.
In this mix we plan for on yoke of the challenges: 1) lodge (point) ascertaining and shelter localization (Fig.
1.2) and 2) characterization in an offline setting (Fig. 1).
We restrict alternative physiognomy and resist a
countenance for job concerning these handfuls of problems from a control-flow perspective. Inappropriate we pretend the guts of the techniques represented in this arrangement on a plastic enroll and done estimate them on a real-life squabble break down from a large Dutch municipality.
A. PURPOSE
Business Operation love affair methods are bewildered wide than simply accessory tasks stray consider the resources of an organization to achieve a defined Relationship outcome. Business methods fundamentally be upon non-native a mid of perspectives, above the oversee flow, data, and the resource perspectives. In today‟s active marketplace, it is increasingly keys for enterprises to update their methods consequence as to trim cost and to improve performance.
Therefore flexibility and modify have been studied in- depth in the context of business method supervision (BPM). For there is a need for techniques that deal with such second-order dynamics. Analyzing such modifies is of utmost importance when supporting or improving operational methods and to obtain an accurate insight on method executions at any instant of time. When dealing with concept drifts in method mining the following three main challenges emerge.
1) Modify point detection: The first and most fundamental problem is to detect concept drift in methods i.e. to detect that a method modify has taken place. If so the next step is to identify the time periods at which modifies have taken place. For example by analyzing an occurrence log from an organization (deploying seasonal methods) we should be able to detect that method modifies happen and that these modifies happen at the onset of a season.
2) Modify localization and characterization: Once a point of modify has been identified the next step is to characterize the nature of modify and identify the region(s) of modify (localization) in a method.
Uncovering the nature of modify is a challenging problem that involves both the identification of modify perspective (e.g. control flow data resource sudden gradual and so on) and the identification of the exact modify itself.
II. LITERATURE SURVEY
A. INTRODUCTION
A literature survey or literature review means study of references papers and old algorithms that we have read for manipulative the recommended methods. It also helps in exposure summarization of all the old references papers their disadvantages. The complete literature survey for the project helps in evaluating and
distinct numerous methods algorithms in numerous ways that have executed in the reconsider.
The literature study arranged in this reconsider of the project sustains high accessibility of data several algorithms numerous old references papers evaluation of the methods. This design sustains numerous types of cloud techniques and methods are studied.
B. LITERATURE STUDY
In this section we discuss the basic concepts in method mining and concept drifts in data mining/machine learning. A. Method Mining Method mining serves a bridge among modeling. Business methods leave trails in a variety of data sources (e.g. audit trails databases and transaction logs). Method mining aims at determining monitoring and improving real methods by extracting knowledge from occurrence logs recorded by a variety of systems (ranging from sensor networks to enterprise information systems). The starting point for method mining is an occurrence log which is a collection of occurrences. We assume that occurrences can be related to method instances (often called cases) and are described by some activity name. The occurrences within a method instance are ordered.
Conformance deals with comparing an a priori method model with the observed behavior as recorded in the log and aims at detecting inconsistencies/ deviations among a method model and its corresponding execution log. In other words it checks for any violation among what was expected to happen and what actually has happened.
Enhancement deals with extending or improving an existing model based on information about the method execution in an occurrence log. For example a notating a method model with performance data to show bottlenecks throughput times and so on.
Being a relatively young research discipline several method mining challenges remain to be addressed. The method mining manifesto lists 11 challenges. The fourth challenge is dealing with concept drift and thus far a little work has been done on this highly relevant topic.
B. Concept Drift Concept drift in machine learning and data mining refers to situations when the relation among the input data and the target variable which the model is trying to predict modifies occurrence ally in unforeseen ways. Therefore the accuracy of the predictions may degrade occurrence ally. To reoccurrence that predictive models need to be able to adapt online i.e. to update themselves regularly with new data. The setting is typically looped over an infinite data stream as follows:
1) Receive new data;
2) make a prediction;
3) Receive feedback (the true target value); and
________________________________________________________________________________________________
4) Update the predictive model.
While operating under such circumstances predictive models are necessary: 1) to react to concept drift (and adapt if needed) as soon as possible; 2) to distinguish drifts from once-off noise and adapt to modifies but be robust to noise; and 3) to operate in less than data arrival time and use limited memory for storage. In this setting many adaptive algorithms have been developed (e.g.
over views).
C. EXISTING SYSTEM
The method is stable and enough example traces have been recorded in the occurrence log it is possible to determine a high quality method model that can be used for performance analysis compliance checking and prediction.
Unfortunately most methods are not in steady- state. In today's dynamic marketplace it is increasingly necessary for enterprises to streamline their methods so as to reduce costs and to improve performance.
D. DISADVANTAGES OF EXISTING SYSTEM
The description and problem in not an offline because of enhanced setting.
To provide a new detection concept drift in methods i.e. to detect that a method modify has taken place.
Modify localization and characterization.
Modify the proposed method determine: Which is having identified localized and characterized and modifies it is necessary to put all of these in perspective.
III. SYSTEM ANALYSIS
System analysis explains about proposed scheme and its advantages over existing system. Further it researches on modules framework of proposed architecture with its context level design. System analysis in addition explains about feasible study of the scheme and systems software and hardware requirements.
Systems Analysis is a detailed study of current improvement information throughout numerous steps procedures functions and entities which including in getting the analysis of computer Information Current improvement Information Algorithm Information and Additional Inner and Outer information related to the projected study. System Analysis makes available a series of scientific technique to understand the numerous requirements necessary for intending the current improvement work. In System analysis we study about numerous functional non functional requirements
desirable for the intending the projected system. In the current System Analysis is we have studied numerous papers related to the current improvement work and planned the intend using numerous tools such as Class Diagrams Sequence Diagrams data flow diagrams and data dictionary are used in increasing a logical model of system.
A. DO MOST IMPORTANT ANALYSIS
The selected area or do most important analysis is the method studying which software to be selected for intending the current improvement work. The word „do most important‟ in the case means the general field of business or technology in which the customers expect to be using the software. As per our requirement the current improvement is related to cryptographic and wireless protocol supervision to intend these define iterations we selected java technology because it make available wireless security and network packages.
B. REQUIREMENT ANALYSIS
A requirement analysis is a study of numerous technique and functions like man power software inputs outputs and meth ding to be executed for the improvement of projected system. In this study we have carry ousted functional and non functional requirements for the current improvement.
C. FUNCTIONAL REQUIREMENTS
Functional requirements describe what the system requires. The functional requirements are the detailed study of what inputs outputs data and calculations to be carry ousted. In the current improvement we carry out the given input output and data calculations.
D. NON-FUNCTIONAL REQUIREMENTS
Non-functional requirements are the constraints that must be adhered throughout improvement. They bound what hold back can be used and set bounds on aspects of the software‟s quality.
1) User Interfaces
The User Interface is a GUI developed using Java and Net beans.
2) Software Interfaces
The most important method is done in java using Java swings Java RMI Java AWT and java input and output packages.
E. PROPOSED SYSTEM
In this project we have introduced the topic of concept drift in method mining i.e. analyzing method modifies based on occurrence logs.
We proposed feature sets and techniques to successfully detect that modifies in occurrence logs and identify the regions of modify in a method.
F. ADVANTAGES OF PROPOSED SYSTEM
Heterogeneity of cases arising because of method modifies can be successfully dealt with by detecting concept drifts.
Supporting or improving operational methods and to obtain an accurate insight on method executions at any instant of time.
G. SYSTEM STUDY
The feasibility study is an estimation and analysis of the numerous potential requirements of a projected project which is based on erroneous and extensive investigation and advanced reexamine work to sustain the method of good decision making. Feasibility Study is detailed study of making analysis and gathering information for increasing the project. A viability interpret is drive erroneously to feign the scourge corpus juries that meets carry outrace requirements. The filthy pointing of the workability interpret sortie is to establish inevitably it would be economically and technically base to develop the forecast. The practicality criticize skirmish involves the dissection of the calling and gathering of through output befitting answer voice-over to the product such as the surrogate details truly which would be input to the criterion criteria the method scheduled to be hassle overseas on these details the procure text destined to be come up by the customs as extensively as numerous constraints on the behavior of the system.
This study gives information regarding Technical Information Economical or Cost Information Operational Study Social and numerous additional studies which are feasible in designing the project or not. The major areas considered in feasibility analysis are as follows.
1. Economic Feasibility 2. Technical feasibility 3. Operational Feasibility H. ECONOMIC FEASIBILITY
The purpose of the economic feasibility appraisal is to conclude the positive economic benefits related cost expenditure and additional most important to the
organization that the recommended system will make available. It includes numerous expenditures and budges related to quantification and identification of all the economic requirements for designing the project which is expected. This estimation naturally involves a cost benefits analysis. Budgetary dissection is the worst a lot of times worn advance for evaluating the act of a minimal corpus juries. From a recommended system and compare them with cut corners. If parsimonious preponderate over costs arbitration is phony to obstruction and apply the system. Made if it is to essay a fluke of being improved. This is a leisurely commitment go improves in correctness at till the end of time epoch of the system life cycle.
I. TECHNICAL FEASIBILITY
In technical feasibility study we focus on the system requirements for improvement of the project. It is technically feasible to design the project as the total modules described in the modules description can be created using Front-End interaction JSP and Tomcat Server activities using Java 1.7. As the project modules are focused on wireless activities java supports J2ME Java Mobile Edition packages for wireless programming J2EE Java Enterprise Edition packages for Networking programming. To implement the project we have selected the given technical environment we require Pentium/Core-2 Duo Method or with 2 GB Ram and 80 GB Hard disk and Java Programming language. This is wary connected with naming outfit and software go wool-gathering will successfully satisfy the user requirement. The applied needs of the code may alter lengths but power figure out:
• The capacity to upon forth entangled with outputs in a predisposed maturity.
• Acceptance time under outright conditions.
• Gifts to force a certain lot of deal at a precise speed.
• Gift to fool around figures to distant locations.
In examining polytechnic applicability proportion of the patterns is given concerning economic statement than the actual make of hardware. The arrangement obligated to give the totalitarian upset relative to the system‟s Riviera: Regardless unlike workstations are directed in whatever way these units are interconnected so that they might operate and communicate smoothly. What speeds of input and glean have to be achieved at exacting quality of printing.
J. OPERATIONAL FEASIBILITY
Our application makes available Graphical Interface for the end user and which very easy and feasible to operate.
The front end navigations are created using Java swings
________________________________________________________________________________________________
which make available very easy to the user to enter the necessary information and get the necessary outputs.
The project is highly user interactive application and network based. The project is making available with numerous windows buttons and additional graphical navigations so that the system is fully operationally feasible.
Execute workability is a feign of how in the world on earth sufficiently a minor principles solves the demands and takes assessment of the opportunities identified at near tract intelligibility and how it satisfies the concatenate identified in the requirements analysis phase of customs improvement. The impact applicability onus focuses on the erroneousness to which the supposititious advance projects fits in to the present intrigue sky and objectives yon shrink from to earlier b Earlier schedule delivery date corporate culture and existing business methods. To certain accomplishment marketability influence outcomes keep be imparted throughout obstruction and improvement. These parameters are booked to be meditating on at the primitive early of design if requirement conclusion behaviors are to be realized. A encipher design and improvement requires entertain and advantageous be attractive to of strategy and administering efforts to meet the previously mentioned parameters. A system may rejoinder its premeditated desire rout well instantaneously its technical and operating characteristics are engineered into the design.
This is in the sky escort to material organizational and political aspects. The truth to be steady is:
• What modify stability be brought back the rules?
• What organizational structure is disturbed?
• What ground-breaking skills stability be necessary?
Carry off the authentic ally personnel have these skills?
If remote origin they be fragmented in seemly for course of time?
This workability critique is sit on broadly by a compendious rank of relatives who are common with suspicion maxims technique and are skilled in pandect analysis and design method. In name unsurpassed projects are profitable only if they butt be decayed into clue system drift will meet the operating requirements of the organization. This enroll of viability asks if the system will operate tout de suite it is developed and installed.
K. STEPS IN FEASIBILITY ANALYSIS
In implementation of project the following seven steps are involved:
From a project team appoints a project leader.
Prepare system flow chart.
Enumerate potential candidate system
Describe the uniqueness characteristics of candidate system.
Conclude the evaluate carry outrace and cost successfulness of each candidate system.
Weigh system carry outrace and cost data.
Select the best system.
L. MODULE DESIGN AND ORGANIZATION
Feature extraction and selection
Generate populations
Compare populations
Interactive visualization
Analyze modifies
M. MODULES DESCRIPTION Feature extraction and selection:
This step pertains in defining the characteristics of the traces in an occurrence log. In this paper we have defined four features that characterize the control-flow perspective of method instances n an occurrence log.
Depending on the focus of analysis we may define additional features e.g. if we are interested in analyzing modifies in organizational/resource perspective we may consider features derived from social networks as a means of characterizing the occurrence log. In addition to feature extraction this step also involves feature selection. Feature selection is important when the number of features extracted is large.
Generate populations:
An occurrence log can be transformed into a data stream based on the features selected in the previous step. This step deals with defining the sample populations for studying that modifies in the characteristics of traces.
Different criteria/scenarios may be considered for generating these populations from the data stream. We have considered non-overlapping continuous and fixed- size windows for defining the populations. We may also consider for example non-continuous windows (there is a gap among two populations) adaptive windows (windows can be of different lengths) and so on which are more appropriate for dealing with gradual and recurring drifts.
Compare populations:
Once the sample populations are generated the next step is to analyze these populations for any modify in characteristics. In this paper we advocate the use of statistical hypothesis tests for comparing populations.
The null hypothesis in statistical tests states that distributions (or means or standard deviations) of the two sample populations are equal. Depending on desired assumptions and the focus of analysis different statistical tests can be used.
Interactive visualization:
The results of comparative studies on the populations of trace characteristics can be intuitively presented to an analyst. For example the significance probabilities of the hypothesis tests can be visualized as a drift plot.
Troughs in such a drift plot signify a modify in the significance probability thereby implying a modify in the characteristics of traces.
Analyze modifies:
Visualization techniques such as the drift plot can assist in identifying the modify points. Having identified that modify had taken place this step deals with techniques that assist an analyst in characterizing and localizing that modify and in determining the modify method.
CONTEXT DIAGRAM OF PROJECT
Fig: System Architecture
IV. IMPLEMENTATION
A. Java Technology
Java technology is together a programming language and a platform.
V. SYSTEM TESTING
A. TEST CASES Table: +ve Test Cases
Table: -ve Test Cases
VI. SYSTEM RESULTS
Screen for Users Business page
VII. CONCLUSION & FUTURE ENHANCEMENT
A. CONCLUSION
In this project we have introduced the topic of concept drift in method mining i.e. analyzing method modifies based on occurrence logs. We proposed feature sets and techniques to successfully detect the modifies\s in occurrence logs and identify the regions of modify in a method. Our initial results show that heterogeneity of cases arising because of method modifies can be successfully dealt with by detecting concept drifts. Once modify points are identified the occurrence log can be partitioned and analyzed. This is the first step in the direction of dealing with modifies in any method monitoring and analysis efforts. We have considered modifies only with respect to the control flow perspective manifested as sudden and gradual drifts.
Therefore our analysis should only be observed as the starting point for a new subfield in the method mining domain and there are lots of challenges that still need to be addressed. Some of these challenges include.
________________________________________________________________________________________________
B. FUTURE ENHANCEMENT
This project is an attempt to enhance the existing Drift Detection with Modify method determined in complex Datasets. After detecting the modify points and the regions of modify it is necessary to put them together in perspective. Organizations would be interested in determining the evolution of modify (e.g. as an animation depicting how the method has modified/evolved occurrence ally). In addition there are other applications such as deriving a configurable model for the method variants. A configurable method model describes a family of similar method models. The method variants determined using concept drift can be merged to derive a configurable method model.
REFERENCES
[1] (2010). All-in-one Permit for Physical Aspects:
(Omgevingsvergunning) in a Nutshell [Online].
Available:
http://www.answersforbusiness.nl/regulation/all- in-one-permit-physical-aspects
[2] United States Code. (2002, Jul.). Sarbanes-Oxley Act of 2002, PL 107-204, 116 Stat 745 [Online].
Available: http://files.findlaw.com/news.
findlaw.com/cnn/
docs/gwbush/sarbanesoxley072302.pdf
[3] W. M. P. van der Aalst, M. Rosemann, and M.
Dumas, “Deadline-based escalation in method- aware information systems,” Decision Support Syst., vol. 43, no. 2, pp. 492–511, 2011.
[4] M. Dumas, W. M. P. van der Aalst, and A. H. M.
Ter Hofstede, Method- Aware Information Systems: Bridging People and Software Through Method Technology. New York, NY, USA:
Wiley, 2005.
[5] W. M. P. van der Aalst and K. M. van Hee, Workflow Supervision: Models, Methods, and Systems. Cambridge, MA, USA: MIT Press, 2004.
[6] W. M. P. van der Aalst, Method Mining:
Determiney, Conformance and Enhancement of Business Methodes. New York, NY, USA:
Springer-Verlag, 2011.
[7] B. F. van Dongen and W. M. P. van der Aalst, “A meta model for method mining data,” in Proc.
CAiSE Workshops (EMOI-INTEROP
Workshop), vol. 2. 2005, pp. 309–320.
[8] C. W. Günther, (2009). XES Standard Definition [Onlilne]. Available: http://www.xes- standard.org
[9] F. Daniel, S. Dustdar, and K. Barkaoui, “Method mining manifesto,” in BPM 2011 Workshops, vol. 99. New York, NY, USA: Springer-Verlag, 2011, pp. 169–194.
[10] R. P. J. C. Bose, W. M. P. van der Aalst, I.
Žliobait˙e, and M. Pechenizkiy, “Handling concept drift in method mining,” in Proc. Int.
CAiSE, 2011, pp. 391–405.
[11] J. Carmona and R. Gavaldà, “Online techniques for dealing with concept drift in method mining,”
in Proc. Int. Conf. IDA, 2012, pp. 90–102.
[12] J. Schlimmer and R. Granger, “Beyond incremental methoding: Tracking concept drift,”
in Proc. 15th Nat. Conf. Artif. Intell., vol. 1.
1986, pp. 502–507.
[13] A. Bifet and R. Kirkby. (2011). Data Stream Mining: A Practical Move towards, University of Waikato, Waikato, New Zealand [Online].
Available:
http://www.cs.waikato.ac.nz/~abifet/MOA/Strea mMining.pdf
[14] I. Žliobait˙e, “Learning under concept drift: An Overview,” CoRR, vol. abs/1010.4784, 2010
[Online]. Available:
http://arxiv.org/abs/1010.4784
[15] J. Gama, P. Medas, G. Castillo, and P. Rodrigues,
“Learning with drift detection,” in Proc. SBIA, 2004, pp. 286–295.
[16] A. Bifet and R. Gavaldà, “Learning from time- changing data with adaptive windowing,” in Proc. 7th SIAM Int. Conf. Data Mining (SDM), 2007, pp. 443–448.
[17] G. J. Ross, N. M. Adams, D. K. Tasoulis, and D.
J. Hand, “Exponentially weighted moving average charts for detecting concept drift,”
Pattern Recognit. Lett., vol. 33, no. 2, pp. 191–
198, 2012.
[18] K. Nishida and K. Yamauchi, “Detecting concept drift using statistical testing,” in Proc. 10th Int.
Conf. Determiney Sci.. 2007, pp. 264–269.
[19] C. Alippi and M. Roveri, “Just-in-time adaptive classifiers–Part I: Detecting nonstationary modifys,” IEEE Trans. Neural Netw., vol. 19, no.
7, pp. 1145–1153, Jul. 2008.
