Swedish Institute of Computer Science Publications Database
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Scalable in-network rate monitoring
No full textWe propose a highly scalable statistical method for modelling the monitored traffic rate in a network node and suggest a simple method for detecting increased risk of congestion at different monitoring time scales. The approach is based on parameter estimation of a lognormal distribution using the method of moments. The proposed method is computation- ally efficient and requires only two counters for updating the parameter estimates between consecutive inspections. Evaluation using a naive congestion detector with a success rate of over 98% indicates that our model can be used to detect episodes of high congestion risk at 0.3 s using estimates captured at 5 m intervals
A Modelling Pearl with Sortedness Constraints
No full textSome constraint programming solvers and constraint modelling languages feature the Sort(L,P,S) constraint, which holds if S is a nondecreasing rearrangement of the list L, the permutation being made explicit by the optional list P. However, such sortedness constraints do not seem to be used much in practice. We argue that reasons for this neglect are that it is impossible to require the underlying sort to be stable, so that Sort cannot be guaranteed to be a total-function constraint, and that L cannot contain tuples of variables, some of which form the key for the sort. To overcome these limitations, we introduce the StableKeysort constraint, decompose it using existing constraints, and propose a propagator. This new constraint enables a powerful modelling idiom, which we illustrate by elegant and scalable models of two problems that are otherwise hard to encode as constraint programs
A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
No full textHeavy-duty machines are equipment constructed for working under rough conditions and their design is meant to withstand heavy workloads. However, the last decades technical development in cheap electronically components have lead to an increase of electrical systems in traditionally mainly mechanical systems of heavy-duty machines. As the complexity of these machines increases, so does the complexity of detecting and diagnosing machine faults. However, the addition of new electrical systems, such as on-board computational power and telematics, makes it possible to add new sensors that measure signals relevant for fault detection and diagnosis, and to process signals on-board or off-board the machines.In this thesis, we address the diagnostic problem by investigating data-driven methods for remote diagnosis of heavy-duty machines, where a part of the analysis is performed on-board the machine (fault detection), while another part is performed off-board the machine (fault classification). We propose a diagnostic framework where we use a novel combination of methods for each step in the diagnosis. On-board the machine, we have used logistic regression as an anomaly detector to detect faults that will lead to a stream of individual cases classified as anomalous or not. Then, either on-board or off-board, we can use a probabilistic anomaly detector to identify whether the stream of cases is truly anomalous when we look at the stream of cases as a group. The anomalous group of cases is called a composite case. Thereafter, off-board the machine, each anomalous individual case is classified into a fault type using a case-based reasoning approach to fault diagnosis. In the final step, we fuse the individual classifications into a single aggregated classification for the composite case. In order to be able to assess the reliability of a diagnosis, we also propose a novel case-based approach to estimating the reliability of probabilistic predictions. It can, for instance, be used for assessing the confidence of the classification of a composite case given historical data of the predictive reliability
Health Data: Representation and (In)visibility
No full textHealth data requires context to be understood. I show how, by examining two areas: self-surveillance, with a focus on representation of bodily data, and mass-surveillance, with a focus on representing populations. I critically explore how Information and Communication Technology (ICT) can be made to represent individuals and populations, and identify implications of such representations. My contributions are: (i) the design of a self-tracking stress management system, (ii) the design of a mass-surveillance system based on mobile phone data, (iii) an empirical study exploring how users of a fitness tracker make sense of their generated data, (iv) an analysis of the discourse of designers of a syndrome surveillance system, (v) a critical analysis of the design process of a mass-surveillance system, and (vi) an analysis of the historicity of the concepts and decisions taken during the design of a stress management system. I show that producing health data, and subsequently the technological characteristics of algorithms that produce them depend on factors present in the ICT design process. These factors determine how data is made to represent individuals and populations in ways that may selectively make invisible parts of the population, determinants of health, or individual conception of self and wellbeing. In addition, I show that the work of producing data does not stop with the work of the engineers who produce ICT-based systems: maintenance is constantly required
Deriving Cellular Network Structure From Inferred Handovers in a Cellular Association Trace
No full textA cellular association trace consists of timestamped events
recording user activity in labeled cells in a cellular network.
From such data one can infer that if a user appears in two different cells within a short span of time, that a handover took place, and that the coverage areas of the two cells overlap.
That is, one can infer geographic information from handover
behavior. One would like to expand this kind of inference to
a larger scale, perhaps reconstructing a proximity graph of
the cellular sites, or creating an approximate 2-dimensional
embedding of the cells. We have analyzed a large-scale cellular association trace of several months of activity for several million users on a 3G network, and have found that handover behavior is actually incredibly diverse and complicated, making it very dicult to make any sort of global inferences, even in small sections of a network. In this paper we present some stable elements of handover behavior, and present several methods one can use to extract proximity
information from such a trace
Knowing an Object by the Company It Keeps: A Domain-Agnostic Scheme for Similarity Discovery
No full textAppropriately 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
