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  • 1. Arvidsson, Åke
    et al.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Tracking user terminals in a mobile communication network2011Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    There is provided a method of tracking user terminals in a mobile communication network. The method comprising, at a tracking node, determining that a user terminal is located in a tracking area, storing data associated with the tracking area, the data comprising a number of observations of all user terminals at the tracking area at a first time, receiving a page response from the user terminal located in one of the tracking area and a further tracking area, and in the event that the user terminal remains located at the tracking area, updating the data to include the number of page responses received at the tracking area after a first time interval, and in the event that the user terminal is located at the further tracking area, updating the data to include the number of page responses received at the further tracking area after the first time interval.

  • 2.
    Bjurling, Björn
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Translation of Probabilistic QoS in a Decentralized Hierarchical Setting2011Conference paper (Refereed)
  • 3.
    Boman, Magnus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Forsell, Erik
    Karolinska Institute, Sweden; Stockholm County Council, Sweden.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden, ICT, SICS.
    Görnerup, Olof
    RISE - Research Institutes of Sweden, ICT, SICS.
    Isacsson, Nils
    Karolinska Institute, Sweden; Stockholm County Council, Sweden.
    Sahlgren, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Kaldo, Viktor
    Karolinska Institute, Sweden; Stockholm County Council, Sweden; Linnaeus University, Sweden.
    Learning machines in Internet-delivered psychological treatment2019In: Progress in Artificial Intelligence, ISSN 2192-6352Article in journal (Refereed)
    Abstract [en]

    A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

  • 4.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Learning machines for computational epidemiology2014Conference paper (Refereed)
    Abstract [en]

    Resting on our experience of computational epidemiology in practice and of industrial projects on analytics of complex networks, we point to an innovation opportunity for improving the digital services to epidemiologists for monitoring, modeling, and mitigating the effects of communicable disease. Artificial intelligence and intelligent analytics of syndromic surveillance data promise new insights to epidemiologists, but the real value can only be realized if human assessments are paired with assessments made by machines. Neither massive data itself, nor careful analytics will necessarily lead to better informed decisions. The process producing feedback to humans on decision making informed by machines can be reversed to consider feedback to machines on decision making informed by humans, enabling learning machines. We predict and argue for the fact that the sensemaking that such machines can perform in tandem with humans can be of immense value to epidemiologists in the future.

  • 5. Ferreira, Diogo
    et al.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    On the Discovery of Process Models from Unlabelled Event Logs2009Conference paper (Refereed)
  • 6. Ferreira, J.J. P.
    et al.
    Antunes, N.S.
    Rabelo, R.J.
    Klen, Alexandra Pereira
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Dynamic Forecasting for Master Production Planning with Stock and Capacity Constraints2003In: E-Business Applications, Germany: Springer Verlag , 2003, 1Chapter in book (Refereed)
  • 7.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    On Practical machine Learning and Data Analysis2008Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis discusses and addresses some of the difficulties associated with practical machine learning and data analysis. Introducing data driven methods in e.g industrial and business applications can lead to large gains in productivity and efficiency, but the cost and complexity are often overwhelming. Creating machine learning applications in practise often involves a large amount of manual labour, which often needs to be performed by an experienced analyst without significant experience with the application area. We will here discuss some of the hurdles faced in a typical analysis project and suggest measures and methods to simplify the process. One of the most important issues when applying machine learning methods to complex data, such as e.g. industrial applications, is that the processes generating the data are modelled in an appropriate way. Relevant aspects have to be formalised and represented in a way that allow us to perform our calculations in an efficient manner. We present a statistical modelling framework, Hierarchical Graph Mixtures, based on a combination of graphical models and mixture models. It allows us to create consistent, expressive statistical models that simplify the modelling of complex systems. Using a Bayesian approach, we allow for encoding of prior knowledge and make the models applicable in situations when relatively little data are available. Detecting structures in data, such as clusters and dependency structure, is very important both for understanding an application area and for specifying the structure of e.g. a hierarchical graph mixture. We will discuss how this structure can be extracted for sequential data. By using the inherent dependency structure of sequential data we construct an information theoretical measure of correlation that does not suffer from the problems most common correlation measures have with this type of data. In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. We describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system. To minimise the effort with which results are achieved within data analysis projects, we need to address not only the models used, but also the methodology and applications that can help simplify the process. We present a methodology for data preparation and a software library intended for rapid analysis, prototyping, and deployment. Finally, we will study a few example applications, presenting tasks within classification, prediction and anomaly detection. The examples include demand prediction for supply chain management, approximating complex simulators for increased speed in parameter optimisation, and fraud detection and classification within a media-on-demand system.

  • 8.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ferreira, Diego
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Estimating the Parameters of Randomly Interleaved Markov Models2009Conference paper (Refereed)
  • 9.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    A brief introduction to hierarchical graph mixtures2004Conference paper (Refereed)
  • 10.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Dependency derivation in industrial process data2001In: Proceedings of the 2001 International Conference on Data Mining, Los Alamitos, California: IEEE Computer Society Press , 2001, 1, , p. 4p. 599-602Conference paper (Refereed)
  • 11.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Finding dependencies and delays in measured data2008In: Use of Modeling and Simulation in Pulp and Paper Industry, COST , 2008, 1Chapter in book (Refereed)
  • 12.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Finding dependencies in industrial process data2002In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, no 50Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    Dependency derivation and the creation of dependency graphs are critical tasks for increasing the understanding of an industrial process. However, the most commonly used correlation measures are often not appropriate to find correlations between time series. We present a measure that solves some of these problems.

  • 13.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Incremental diagnosis with limited historical data2006Conference paper (Refereed)
  • 14.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Mixture models, graphical models, and hierarchical combinations2008In: Use of Modeling and Simulation in Pulp and Paper Industry, COST , 2008, 1Chapter in book (Refereed)
  • 15.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sales prediction with marketing data integration for supply chains2004In: E-Manufacturing: Business Paradigms and Supporting Technologies, Germany: Springer Verlag , 2004, 1Chapter in book (Refereed)
  • 16.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sales prediction with marketing data integration for supply chains2002In: Proceedings of the 18th International Conference on CAD/CAM, robotics and Factories of the Future, Vol. 1, 2002, 1Conference paper (Refereed)
  • 17.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    The gmdl Modeling and Analysis System2004Report (Other academic)
    Abstract [en]

    This report describes the gmdl modeling and analysis environment. gmdl was designed to provide powerful data analysis, modeling, and visualization with simple, clear semantics and easy to use, well defined syntactic conventions. It provides an extensive set of necessary for general data preparation, analysis, and modeling tasks.

  • 18.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Emulating first principles process simulators with learning systems2005In: Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, Germany: Springer , 2005, 1, p. 377-382Chapter in book (Refereed)
  • 19.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Aronsson, Martin
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    An English butler for complex industrial systems2003In: Proceedings of the 16th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2003), Växjö, Sweden: Växjö University Press , 2003, 1, , p. 9p. 405-413Conference paper (Refereed)
  • 20.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Gudmundsson, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Approximating process simulators with learning systems2005Report (Other academic)
    Abstract [en]

    We explore the possibility of replacing a first principles process simulator with a learning system. This is motivated in the presented test case setting by a need to speed up a simulator that is to be used in conjunction with an optimisation algorithm to find near optimal process parameters. Here we will discuss the potential problems and difficulties in this application, how to solve them and present the results from a paper mill test case.

  • 21.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Fault-tolerant incremental diagnosis with limited historical data2006Report (Other academic)
    Abstract [en]

    In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. All relevant information may not be available initially and must be acquired manually or at a cost. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. Here, we will describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system.

  • 22.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Rudström, Åsa
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Preparation and analysis of multiple source industrial process data2005Report (Other academic)
    Abstract [en]

    Industrial process data is often stored in a wide variety of formats and in several different repositories. Efficient methodologies and tools for data preparation and merging are critical for efficient analysis of such data. Experience shows that data analysis projects involving industrial data often spend the major part of their effort on these tasks, leaving little room for model development and generating applications. This paper identifies and classifies the needs and individual steps in data preparation of industrial data. A methodology for data preparation specifically suited for the domain is proposed and a practically useful set of primitive operations to support the methodology is defined. Finally, a proof of concept data preparation system implementing the proposed operations and a scripting facility to support the iterations in the methodology is presented along with a discussion of necessary and desirable properties of such a tool.

  • 23.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Rudström, Åsa
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Data Preparation in Complex Industrial Systems2005In: Condition Monitoring and Diagnostic Engineering Management (COMADEM 2005), Cranfield, UK: Cranfield University Press , 2005, 5, p. 543-551Conference paper (Refereed)
  • 24.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Fault-tolerant incremental diagnosis with limited historical data2008Conference paper (Refereed)
    Abstract [en]

    We describe a novel incremental diagnostic system based on a statistical model that is trained from empirical data. The system guides the user by calculating what additional information would be most helpful for the diagnosis. We show that our diagnostic system can produce satisfactory classification rates, using only small amounts of available background information, such that the need of collecting vast quantities of initial training data is reduced. Further, we show that incorporation of inconsistency-checking mechanisms in our diagnostic system reduces the number of incorrect diagnoses caused by erroneous input.

  • 25. Gonzalez Prieto, Alberto
    et al.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Miron, Avi
    Towards Decentralized Probabilistic Management2011In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 49, p. 80-86Article in journal (Refereed)
  • 26.
    Görnerup, Olof
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden, ICT, SICS.
    Streaming word similarity mining on the cheap2018Conference paper (Other academic)
    Abstract [en]

    Accurately and efficiently estimating word similarities from text is fundamental in natural language processing. In this paper, we propose a fast and lightweight method for estimating similarities from streams by explicitly counting second-order co-occurrences. The method rests on the observation that words that are highly correlated with respect to such counts are also highly similar with respect to first-order co-occurrences. Using buffers of co-occurred words per word to count second-order co-occurrences, we can then estimate similarities in a single pass over data without having to do prohibitively expensive similarity calculations. We demonstrate that this approach is scalable, converges rapidly, behaves robustly under parameter changes, and that it captures word similarities on par with those given by state-of-the-art word embeddings.

  • 27.
    Görnerup, Olof
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Vasiloudis, Theodore
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Domain-Agnostic Discovery of Similarities and Concepts at Scale2017In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 51, p. 531-560Article in journal (Refereed)
    Abstract [en]

    Appropriately defining and efficiently calculating similarities from large data sets are often essential in data mining, both for gaining understanding of data and generating processes, and for building tractable representations. Given a set of objects and their correlations, we here rely on the premise that each object is characterized by its context, i.e. its correlations to the other objects. The similarity between two objects can then be expressed in terms of the similarity between their contexts. In this way, similarity pertains to the general notion that objects are similar if they are exchangeable in the data. We propose a scalable approach for calculating all relevant similarities among objects by relating them in a correlation graph that is transformed to a similarity graph. These graphs can express rich structural properties among objects. Specifically, we show that concepts - abstractions of objects - are constituted by groups of similar objects that can be discovered by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of fields, and will here be demonstrated in three domains: computational linguistics, music and molecular biology, where the numbers of objects and correlations range from small to very large.

  • 28.
    Görnerup, Olof
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Vasiloudis, Theodore
    RISE, Swedish ICT, SICS.
    Knowing an Object by the Company It Keeps: A Domain-Agnostic Scheme for Similarity Discovery2015In: 2015 IEEE International Conference on Data Mining, 2015, 18, p. 121-130, article id 7373316Conference paper (Refereed)
    Abstract [en]

    Appropriately defining and then efficiently calculating similarities from large data sets are often essential in data mining, both for building tractable representations and for gaining understanding of data and generating processes. Here we rely on the premise that given a set of objects and their correlations, each object is characterized by its context, i.e. its correlations to the other objects, and that the similarity between two objects therefore can be expressed in terms of the similarity between their respective contexts. Resting on this principle, we propose a data-driven and highly scalable approach for discovering similarities from large data sets by representing objects and their relations as a correlation graph that is transformed to a similarity graph. Together these graphs can express rich structural properties among objects. Specifically, we show that concepts - representations of abstract ideas and notions - are constituted by groups of similar objects that can be identified by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of domains, and will here be demonstrated for three distinct types of objects: codons, artists and words, where the numbers of objects and correlations range from small to very large.

  • 29.
    Görnerup, Olof
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Autonomous Accident Monitoring Using Cellular Network Data2013Conference paper (Refereed)
    Abstract [en]

    Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions.

  • 30.
    Hess, Andrea
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Marsh, Ian
    RISE - Research Institutes of Sweden, ICT, SICS.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden, ICT, SICS.
    Exploring communication and mobility behaviour of 3G network users and its temporal consistency2015Conference paper (Refereed)
    Abstract [en]

    Over the past decade, telecommunication network operators have more and more realized the added value of data analytics for their network deployment efficiency. Early studies targeted the global network perspective by localizing peak loads, both in terms of area and time period. Due to their higher granularity and information richness, current telecommunication datasets allow increasingly deeper insights into the network activities of the users. Existing network traffic classification studies tend to divide users into groups without considering the transitions between different groups caused by individual behavioral traits, which we expect to show observable regularities. Our approach defines a profiling model that characterizes the user behavior as well as its temporal dynamics from two perspectives: w.r.t. (i) the network load the users generate, and (ii) their mobility patterns. The model is evaluated with two unsupervised clustering algorithms of different complexity (namely, XMeans and EM) by means of a 3G trace dataset from a European operator.

  • 31.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Deviation detection of industrial processes2004In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, no 56, p. 13-14Article in journal (Other (popular science, discussion, etc.))
  • 32.
    Kreuger, Per
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Arvidsson, Åke
    zCap: a zero configuration adaptive paging and mobility management mechanism2013In: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, p. 235-258Article in journal (Refereed)
    Abstract [en]

    Today, cellular networks rely on fixed collections of cells (tracking areas) for user equipment localisation. Locating users within these areas involves broadcast search (paging), which consumes radio bandwidth but reduces the user equipment signalling required for mobility management. Tracking areas are today manually configured, hard to adapt to local mobility and influence the load on several key resources in the network. We propose a decentralised and self-adaptive approach to mobility management based on a probabilistic model of local mobility. By estimating the parameters of this model from observations of user mobility collected online, we obtain a dynamic model from which we construct local neighbourhoods of cells where we are most likely to locate user equipment. We propose to replace the static tracking areas of current systems with neighbourhoods local to each cell. The model is also used to derive a multi-phase paging scheme, where the division of neighbourhood cells into consecutive phases balances response times and paging cost. The complete mechanism requires no manual tracking area configuration and performs localisation efficiently in terms of signalling and response times. Detailed simulations show that significant potential gains in localisation effi- ciency are possible while eliminating manual configuration of mobility management parameters. Variants of the proposal can be implemented within current (LTE) standards.

  • 33.
    Kreuger, Per
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Arvidsson, Åke
    Zero configuration adaptive paging (zCap)2012In: IEEE 76th Veh. Technol. Conf., IEEE , 2012, 14Conference paper (Refereed)
    Abstract [en]

    Today, cellular networks rely on fixed collections of cells (tracking areas) for handset localisation. This management parameter is manually configured and maintained and is not regularly adapted to changes in use patterns. We present a decentralised approach to localisation, based on a self-adaptive probabilistic mobility model. Estimates of model parameters are built from observations of mobility patterns collected on- line using a distributed algorithm. Based on these estimates, dynamic local neighbourhoods of cells are formed and maintained by the mobility management entities of the network. These neighbourhoods replace the static tracking areas used in current implementations by using the tracking area list facility of LTE. The model is also used to derive a multi phase paging scheme, where the division of cells into consecutive phases is optimal with respect to a set balance between response times and paging cost. The approach requires no manual tracking area configuration, and performs localisation efficiently in terms of number of location updates, page messages per localisation request and response times.

  • 34.
    Kreuger, Per
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Görnerup, Olof
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Corcoran, Diarmuid
    Lundborg, Tomas
    Ermedahl, Andreas
    Methods, Nodes and system for enabling redistribution of cell load2015Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    Patent for distributed load balancing mechanism for LTE, developed by SICS in collaboration with Ericsson DURA

  • 35.
    Kreuger, Per
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Görnerup, Olof
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lundborg, Tomas
    Ericsson AB, Sweden.
    Corcoran, Diarmuid
    Ericsson AB, Sweden.
    Ermedahl, Andreas
    Ericsson AB, Sweden.
    Autonomous load balancing of heterogeneous networks2015In: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), 2015, 11, article id 7145712Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for load balancing heterogeneous networks by dynamically assigning values to the LTE cell range expansion (CRE) parameter. The method records hand-over events online and adapts flexibly to changes in terminal traffic and mobility by maintaining statistical estimators that are used to support autonomous assignment decisions. The proposed approach has low overhead and is highly scalable due to a modularised and completely distributed design that exploits self- organisation based on local inter-cell interactions. An advanced simulator that incorporates terminal traffic patterns and mobility models with a radio access network simulator has been developed to validate and evaluate the method.

  • 36.
    Kreuger, Per
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Steinert, Rebecca
    RISE - Research Institutes of Sweden, ICT, SICS.
    Görnerup, Olof
    RISE - Research Institutes of Sweden, ICT, SICS.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden, ICT, SICS.
    Distributed dynamic load balancing with applications in radio access networks2018In: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, no 2Article in journal (Refereed)
    Abstract [en]

    Managing and balancing load in distributed systems remains a challenging problem in resource management, especially in networked systems where scalability concerns favour distributed and dynamic approaches. Distributed methods can also integrate well with centralised control paradigms if they provide high-level usage statistics and control interfaces for supporting and deploying centralised policy decisions. We present a general method to compute target values for an arbitrary metric on the local system state and show that autonomous rebalancing actions based on the target values can be used to reliably and robustly improve the balance for metrics based on probabilistic risk estimates. To balance the trade-off between balancing efficiency and cost, we introduce 2 methods of deriving rebalancing actuations from the computed targets that depend on parameters that directly affects the trade-off. This enables policy level control of the distributed mechanism based on collected metric statistics from network elements. Evaluation results based on cellular radio access network simulations indicate that load balancing based on probabilistic overload risk metrics provides more robust balancing solutions with fewer handovers compared to a baseline setting based on average load.

  • 37.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Funk, Peter
    Xiong, Ning
    Case-Based Reasoning for Explaining Probabilistic Machine Learning2014In: International Journal of Computer Science and Information Technology, Vol. 6, p. 87-101Article in journal (Refereed)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

  • 38.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS. Mälardalen University, Sweden.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Funk, Peter
    Mälardalen University, Sweden.
    Xiong, Ning
    Mälardalen University, Sweden.
    Explaining Probabilistic Fault Diagnosis and Classification using Case-based Reasoning2014Conference paper (Refereed)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.

  • 39.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Källström, Elisabeth
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Funk, Peter
    Lindström, John
    Håkansson, Lars
    Lundin, Joakim
    Svensson, Magnus
    Larsson, Jonas
    Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches2014Conference paper (Refereed)
    Abstract [en]

    This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision making. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely integrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions.

  • 40.
    Steinert, Rebecca
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gestrelius, Sara
    RISE, Swedish ICT, SICS.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    A Distributed Spatio-Temporal Event Correlation Protocol for Multi-Layer Virtual Networks2011Conference paper (Refereed)
  • 41.
    Steinert, Rebecca
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    An initial approach to distributed adaptive fault-handling in networked systems2009Report (Other academic)
    Abstract [en]

    We present a distributed adaptive fault-handling algorithm applied in networked systems. The probabilistic approach that we use makes the proposed method capable of adaptively detect and localize network faults by the use of simple end-to-end test transactions. Our method operates in a fully distributed manner, such that each network element detects faults using locally extracted information as input. This allows for a fast autonomous adaption to local network conditions in real-time, with significantly reduced need for manual configuration of algorithm parameters. Initial results from a small synthetically generated network indicate that satisfactory algorithm performance can be achieved, with respect to the number of detected and localized faults, detection time and false alarm rate.

  • 42.
    Steinert, Rebecca
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Distributed detection of latency shifts in networks2009Report (Other academic)
    Abstract [en]

    We present the extension of a distributed adaptive fault-detection algorithm applied in networked systems. In previous work, we developed an approach to probabilistic detection of communication faults based on measured probe response delays and packet drops. The algorithm is here extended to detect network latency shifts and adapt to long-term changes of the expected probe response delay. Initial performance tests indicate that detected latency shifts and communication faults successfully can be localised to links and nodes. Further, the amount of network traffic produced by the algorithm scales linearly with the network size.

  • 43.
    Steinert, Rebecca
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Long-term adaptation and distributed detection of local network changes2010Conference paper (Refereed)
    Abstract [en]

    We present a statistical approach to distributed detection of local latency shifts in networked systems. For this purpose, response delay measurements are performed between neighbouring nodes via probing. The expected probe response delay on each connection is statistically modelled via parameter estimation. Adaptation to drifting delays is accounted for by the use of overlapping models, such that previous models are partially used as input to future models. Based on the symmetric Kullback-Leibler divergence metric, latency shifts can be detected by comparing the estimated parameters of the current and previous models. In order to reduce the number of detection alarms, thresholds for divergence and convergence are used. The method that we propose can be applied to many types of statistical distributions, and requires only constant memory compared to e.g., sliding window techniques and decay functions. Therefore, the method is applicable in various kinds of network equipment with limited capacity, such as sensor networks, mobile ad hoc networks etc. We have investigated the behaviour of the method for different model parameters. Further, we have tested the detection performance in network simulations, for both gradual and abrupt shifts in the probe response delay. The results indicate that over 90% of the shifts can be detected. Undetected shifts are mainly the effects of long convergence processes triggered by previous shifts. The overall performance depends on the characteristics of the shifts and the configuration of the model parameters.

  • 44.
    Steinert, Rebecca
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Performance evaluation of a distributed and probabilistic network monitoring approach2012Conference paper (Refereed)
  • 45.
    Steinert, Rebecca
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Performance Evaluation of a Distributed and Probabilistic Network Monitoring Approach2012Conference paper (Refereed)
    Abstract [en]

    We investigate the effects of employing a proba- bilistic fault detection approach relative the performance of a deterministic network monitoring method. The approach has its foundation in probabilistic network management, in which performance limits and thresholds are specified in terms of e.g. probabilities or belief values. When combined with adap- tive mechanisms, probabilistic approaches can potentially offer improved controllability, adaptivity and reliability, compared to deterministic monitoring methods. Results from synthetically generated and real network QoS measurements indicate that the probabilistic approach generally can perform at least as good as a deterministic algorithm, with a higher degree of predictable performance and resource-efficiency. Due to the stochastic nature of the algorithm, worse performance than expected is sometimes observed. Nevertheless, the results give additional support to some of the practical benefits expected in using probabilistic approaches for network management purposes.

  • 46.
    Steinert, Rebecca
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Towards Distributed and Adaptive Detection and Localisation of Network Faults2010Conference paper (Refereed)
    Abstract [en]

    We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of network faults. A detected fault is isolated to a node or link by collaborative fault-localisation. From local measurements obtained through probing between nodes, probe response delay and packet drop are modelled via parameter estimation for each link. Estimated model parameters are used for autonomous configuration of algorithm parameters, related to probe intervals and detection mechanisms. Expected fault-detection performance is formulated as a cost instead of specific parameter values, significantly reducing configuration efforts in a distributed system. The benefit offered by using our algorithm is fault-detection with increased certainty based on local measurements, compared to other methods not taking observed network conditions into account. We investigate the algorithm performance for varying user parameters and failure conditions. The simulation results indicate that more than 95 % of the generated faults can be detected with few false alarms. At least 80 % of the link faults and 65 % of the node faults are correctly localised. The performance can be improved by parameter adjustments and by using alternative paths for communication of algorithm control messages.

  • 47.
    Xiao, Bin
    et al.
    Stockholm University, Sweden.
    Rahmani, Rahim
    Stockholm University, Sweden.
    Li, Yuhong
    Beijing University of Posts and Telecommunications, China.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kanter, Theo G.
    Stockholm University, Sweden.
    Intelligent data-intensive IoT: A survey2016In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, p. 2362-2368, article id 7925122Conference paper (Refereed)
    Abstract [en]

    The IoT paradigm proposes to connect entities intelligently with massive heterogeneous nature, which forms an ocean of devices and data whose complexity and volume are incremental with time. Different from the general big data or IoT, the data-intensive feature of IoT introduces several specific challenges, such as circumstance dynamicity and uncertainties. Hence, intelligence techniques are needed in solving the problems brought by the data intensity. Until recent, there are many different views to handle IoT data and different intelligence enablers for IoT, with different contributions and different targets. However, there are still some issues have not been considered. This paper will provide a fresh survey study on the data-intensive IoT issue. Besides that, we conclude some shadow issues that have not been emphasized, which are interesting for the future. We propose also an extended big data model for intelligent data-intensive IoT to tackle the challenges.

  • 48. Yanggratoke, Rerngvit
    et al.
    Ahmed, Jawwad
    Ardelius, John
    RISE, Swedish ICT, SICS.
    Flinta, Christofer
    Johnsson, Andreas (Ericsson Research)
    Gillblad, Daniel
    RISE, Swedish ICT, SICS.
    Stadler, Rolf
    Predicting Real-time Service-level Metrics from Device Statistics2014Report (Other academic)
    Abstract [en]

    While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices. Specifically, we collect statistics from a Linux kernel of a server machine and predict client-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach service- independent and unique. While our current lab configuration is simple, our results, gained through extensive experimentation, prove the feasibility of accurately predicting client-side metrics, such as video frame rates and RTP packet rates, often within 10-15% error (NMAE), also under high computational load and across traces from different scenarios.

  • 49.
    Yanggratoke, Rerngvit
    et al.
    KTH Royal Institute of Technology, Sweden.
    Ahmed, Jawwad
    Ericsson Research, Sweden.
    Ardelius, John
    RISE - Research Institutes of Sweden, ICT, SICS.
    Flinta, Christofer
    Ericsson Research, Sweden.
    Johnsson, Andreas
    Ericsson Research, Sweden.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden, ICT, SICS.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    A service-agnostic method for predicting service metrics in real time2018In: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, no 2, article id e1991Article in journal (Refereed)
    Abstract [en]

    We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real-time, client-side service metrics for video streaming and key-value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service-specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning. 

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