Khamisi Kalegele

Four Decades of Data Mining in Network and Systems Management

How has the interdisciplinary data mining field been practiced in Network and Systems Management (NSM)? In Science and Technology, there is a wide use of data mining in areas like bioinformatics, genetics, Web, and, more recently, astroinformatics. However, the application in NSM has been limited and inconsiderable. In this article, we provide an account of how data mining has been applied in managing networks and systems for the past four decades, presumably since its birth. We look into the fields applications in the key NSM activities-discovery, monitoring, analysis, reporting, and domain knowledge acquisition. In the end, we discuss our perspective on the issues that are considered critical for the effective application of data mining in the modern systems which are characterized by heterogeneity and high dynamism.

An Agent-based Network Management System Using Active Information Resources

Abstract An expert network administrator is not always stationed as disasters happen. In that case, it is desirable that a novice administrator is capable of taking part in network recovery operations as well. In this paper, an agent-based network management system in emergency situations is presented. We use the Active Information Resource based Network Management System (AIR-NMS) to relieve the human administrator from parts of her management tasks and present an interface that remotely can control this management system. The effectiveness of the system is demonstrated by experiments using a prototype system. Key words: Active Information Resource (AIR), Network Management System, Knowledge-based Autonomous System, Multiagent System, Disaster Recovery. 1. I NTRODUCTION we have proposed an Active Information Resource (AIR) [4] Network systems have evolved fast and are now both sophisticated and complicated. Therefore, network administrators must have an advanced and broad knowledge in network management in order to operate and maintain their network. At the time of the Great East Japan Earthquake in 2011, network services like IP phone and e-mail were instantly discontinued and network administrators had to repair and restart their networks to get them up running again. However, expert administrators are not always stationed and large and complex networks are likely to have short-handed experts. Hence, it is desirable to make novice administrators also capable of taking part in network recovery operations. An interesting solution to this problem is to implement a network management system (NMS), where intelligent software agents [1] are applied. By automating some management tasks, NMSs can reduce the burden for network management. Most traditional NMSs [2,3] are able to gather network status information and detect faults automatically, but identifying the cause of a fault and recover it is one of the most difficult tasks for novice administrators, since they lack the expertise. In order to solve this problem of the traditional NMS, based NMS, called AIR-NMS [7]. The AIR-NMS consists of two types of AIRs, I -AIR and K AIR, where the former measures status information of various network equipment, and the latter controls network management heuristics of human administrators. In this paper, we introduce a study on a knowledge based support method for autonomous service operations in emergency situations. A mobile network module called ICT unit, which is placed at a suffering area in an emergency situation and provides network services for users in the area, is introduced in this study. Using the ICT units, the network services of the damaged network are able to recover rapidly. To maintain stable operation of ICT units, an intelligent management function of ICT units takes important role. We realize this function based on the AIR-NMS concept to reduce the burden for administrators and to enable even novice administrators to operate complex network services. In Section 2, the concept of the AIR-NMS is introduced. In addition, problems of applying the existing AIR-NMS to ICT units are described. In Section 3, the knowledge-based support scheme using an improved AIR-NMS is explained. The experiments using a prototype system are demonstrated in Section 4. Finally, the conclusion is presented in Section 5. Manuscript received: Sept. 09, 2013 / revised : Nov. 20, 2013 Corresponding Author: kino@riec.tohoku.ac.jp Tel: +81-22-217-5415, Fax: +81-22-217-5415 RIEC, Tohoku University. Japan.

Multiagent–based processing and integration of system data

This paper presents a multiagent–based ETL (Extract, Transform, Load) unit for the processing and integration of system operational data in order to improve its value. Operational data plays a vital role in managing and optimising systems. Although KDD (Knowledge Discovery and Data Mining) techniques and concepts have long existed, it is only now that we are seeing real applications being extended onto network and systems management. However, the massive data pre–processing (e.g. feature extraction and data integration) which is needed prior to putting KDD tools in action, is still limiting the extent of exploitation. We propose and design the multiagent–based ETL unit which uses Support Vector Machine and Natural Language Processing techniques to efficiently extract information features from operational data. The unit uses an mSPIDER algorithm to discover INclusion Dependencies (INDs) which are used to integrate data across its peers within the system. We demonstrate efficiency of the unit and the used approaches using operational data from a mailing system.

On-demand numerosity reduction for object learning

In Internet of Things, softwares shall enable their host objects (everyday-objects) to monitor other objects, take actions, and notify humans while using some form of reasoning. The ever changing nature of real life environment necessitates the need for these objects to be able to generalize various inputs inductively in order to play their roles more effectively. These objects shall learn from stored training examples using some generalization algorithm. In this paper, we investigate training sets requirements for object learning and propose a Stratified Ordered Selection (SOS) method as a means to scale down training sets. SOS uses a new instance ranking scheme called LO ranking. Everyday-objects use SOS to select training subsets based on their capacity (e.g. memory, CPU). LO ranking has been designed to broaden class representation, achieve significant reduction while offering same or near same analytical results and to facilitate faster on-demand subset selection and retrieval for resource constrained objects. We show how SOS outperforms other methods using well known machine learning datasets.

Numerosity Reduction for Resource Constrained Learning

When coupling data mining (DM) and learning agents, one of the crucial challenges is the need for the Knowledge Extraction (KE) process to be lightweight enough so that even resource (e.g., memory, CPU etc.) constrained agents are able to extract knowledge. We propose the Stratified Ordered Selection (SOS) method for achieving lightweight KE using dynamic numerosity reduction of training examples. SOS allows for agents to retrieve differentsized training subsets based on available resources. The method employs ranking-based subset selection using a novel Level Order (LO) ranking scheme. We show representativeness of subsets selected using the proposed method, its noise tolerance nature and ability to preserve KE performance over different reduction levels. When compared to subset selection methods of the same category, the proposed method offers the best trade-off between cost, reduction and the ability to preserve performance.