Mining usage patterns in residential intranet of things

International audienceUbiquitous smart technologies gradually transform modern homes into Intranet of Things, where a multitude of connected devices allow for novel home automation services (e.g., energy or bandwidth savings, comfort enhancement, etc.). Optimizing and enriching the Quality of Experience (QoE) of residential users emerges as a critical differentiator for Internet and Communication Service providers (ISPs and CSPs, respectively) and heavily relies on the analysis of various kinds of data (connectivity, performance , usage) gathered from home networks. In this paper, we are interested in new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous works, to analyze individual device logs of home gateways. Based on multidimensional patterns mining framework, we extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Such fine-grained device usage patterns provide valuable insights for emerging use cases such as an adaptive usage of home devices, and also " things " recommendation

The retail market as a complex system

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Extracting Usage Patterns of Home IoT Devices

International audienceUbiquitous connectivity and smart technologies gradually transform homes into Intranet of Things, where a multitude of connected, intelligent devices allow for novel home automation services. Providing new services for home users (e.g., energy saving automations) and Internet Service Providers (e.g., network management and troubleshooting) requires an in-depth analysis of various kinds of data (connectivity, performance, usage) collected from home networks. In this paper, we explore new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous studies, to analyze individual device logs of home gateways. We introduce a multidimensional patterns mining framework, to extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Our results show that our analytics engine provides valuable insights for emerging use cases such as monitoring for energy efficiency, and “things” recommendation

Multidimensional Association Rules in Boolean Tensors ∗

Popular data mining methods support knowledge discovery from patterns that hold in binary relations. We study the generalization of association rule mining within arbitrary n-ary relations and thus Boolean tensorsinsteadofBooleanmatrices. Indeed, manydatasets of interest correspond to relations whose number of dimensions is greater or equal to 3. However, just a few proposals deal with rule discovery when both the head and the body can involve subsets of any dimensions. A challenging problem is to provide a semantics to such generalized rules by means of objective interestingness measures that have to be carefully designed. Therefore, we discuss the need for different generalizations of the classical confidence measure. We also present th

Multidimensional Association Rules in Boolean Tensors

International audiencePopular data mining methods support knowledge discovery from patterns that hold in binary relations. We study the generalization of association rule mining within arbitrary n-ary relation and thus Boolean tensors instead of Boolean matrices. Indeed, many datasets of interest correspond to relations whose number of dimensions is greater or equal to3. However, just a few proposals deal with rule discovery when both the head and the body can involve subsets of any dimensions. A challenging problem is to provide a semantics to such generalized rules by means of objective interestingness measures that have to be carefully designed. Therefore, we discuss the need for different generalizations of the classical confidence measure. We also present the first algorithm that computes, in such a general framework, every rule that satisfies both a minimal frequency constraint and minimal confidence constraints. The approach is tested on real datasets (ternary and 4-ary relations). We report on a case study that concerns dynamic graph analysis thanks to rules