Memory is a part of cognition. It is essential for an individual to function normally in society. It encompasses an individual's lifetime experience, thus defining his identity. This thesis develops the concept of a machine-based personal memory manager which captures and manages an individual's day-to-day external memories. Rather than accumulating large amounts of data which has to be mined for useful memories, the machine-based memory manager automatically organizes memories as they are captured to enable their quick retrieval and use. The main functions of the machine-based memory manager envisioned in this thesis are the support and the augmentation of an individual's biological memory system. In the thesis, a model for a machine-based memory manager is developed. A visual programming environment, which can be used to build context aware applications as well as a proof-of-concept machine-based memory manager, is conceptualized and implemented. An experimental machine-based memory manager is implemented and evaluated. The model describes a machine-based memory manager which manages an individual's external memories by context. It addresses the management of external memories which accumulate over long periods of time by proposing a context aware file system which automatically organizes external memories by context. It describes how personal memory management can be facilitated by machine using six entities (life streams, memory producers, memory consumers, a memory manager, memory fragments and context descriptors) and the processes in which these entities participate (memory capture, memory encoding and decoding, memory decoding and retrieval). The visual programming environment represents a development tool which contains facilities that support context aware application programming. For example, it provides facilities which enable the definition and use of virtual sensors. It enables rapid programming with a focus on component re-use and dynamic composition of applications through a visual interface. The experimental machine-based memory manager serves as an example implementation of the machine-based memory manager which is described by the model developed in this thesis. The hardware used in its implementation consists of widely available components such as a camera, microphone and sub-notebook computer which are assembled in the form of a wearable computer. The software is constructed using the visual programming environment developed in this thesis. It contains multiple sensor drivers, context interpreters, a context aware file system as well as memory retrieval and presentation interfaces. The evaluation of the machine-based memory manager shows that it is possible to create a machine which monitors the states of an individual and his environment, and manages his external memories, thus supporting and augmenting his biological memory.

Diploma thesis focuses on in-memory data processing, its use in reporting and Business Intelligence (BI) in general. The main goal of the theoretical part is to introduce the in memory principles, highlight the differences from hard drive data processing and overview possible implementations of in-memory technology in BI solution. The output of this section is an analysis of advantages and disadvantages of in-memory solutions in various perspectives. The practical part of the thesis consists of the performance benchmark that compares the performance of data processing using the in-memory principles and conventional hard drive methods. The performance comparison is realized in the reporting tools environment, QlikView for in-memory approach and Reporting Services for hard drive based method. Several data sets are used for testing in both mentioned tools. End of the chapter provides the assessment of testing results and discusses the strengths and weaknesses of both principles of data processing. The conclusion of this work discusses the advantages and disadvantages of in-memory data processing and defines the key questions that company management should ask before investing in innovation of the present BI solution. Moreover the conclusion contains recommendations for possible further follow-up work.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)