A Domain-Specific Approach To Unifying The Many Dimensions of Context-Aware Home Service Development

The complete proceedings are available at http://www.smart-world.org/2018/uic/conferenceProceedings.phpInternational audienceDeveloping context-aware homes involves a range of stakeholders, addressing many dimensions such as service design and development, infrastructure deployment, and maintenance. Such a variety of dimensions often translate into heterogeneous, low-level, silo-based processing of sensor data to extract context information. This paper analyzes a range of existing data processing layers in the domain of aging in place to identify key concepts and operations specific to context-aware processing. Based on this analysis, we introduce a context-aware, domain-specific language and its software architecture, which allow to put in synergy the stakeholders of a context-aware home by providing them with a unified approach to designing and developing services. Our approach offers context aware-specific abstractions and notations, within a data-centric and data-driven paradigm. We have validated our approach by applying it to an assisted living platform for aging in place, deployed in the home of 129 users. In particular, we used our domain-specific language to re-implement 53 existing services, originating from the stakeholders of the assisted living platform. These services were deployed and successfully tested for their effectiveness in performing the specific tasks of the stakeholders, such as detection of daily activities, user risk situations, or sensor failures

A Language for Online State Processing of Binary Sensors, Applied to Ambient Assisted Living

International audienceThere is a large variety of binary sensors in use today, and useful context-aware services can be defined using such binary sensors. However, the currently available approaches for programming context-aware services do not conveniently support binary sensors. Indeed, no existing approach simultaneously supports a notion of state, central to binary sensors, offers a complete set of operators to compose states, allows to define reusable abstractions by means of such compositions, and implements efficient online processing of these operators. This paper proposes a new language for event processing specifically targeted to binary sensors. The central contributions of this language are a native notion of state and semi-causal operators for temporal state composition including: Allen's interval relations generalized for handling multiple intervals, and temporal filters for handling delays. Compared to other approaches such as CEP (complex event processing), our language provides less discontinued information, allows less restricted compositions, and supports reusable abstractions. We implemented an interpreter for our language and applied it to successfully rewrite a full set of real Ambient Assisted Living services. The performance of our prototype interpreter is shown to compete well with a commercial CEP engine when expressing the same services

Robust Complex Event Pattern Detection over Streams

Event stream processing (ESP) has become increasingly important in modern applications. In this dissertation, I focus on providing a robust ESP solution by meeting three major research challenges regarding the robustness of ESP systems: (1) while event constraint of the input stream is available, applying such semantic information in the event processing; (2) handling event streams with out-of-order data arrival and (3) handling event streams with interval-based temporal semantics. The following are the three corresponding research tasks completed by the dissertation: Task I - Constraint-Aware Complex Event Pattern Detection over Streams. In this task, a framework for constraint-aware pattern detection over event streams is designed, which on the fly checks the query satisfiability / unsatisfiability using a lightweight reasoning mechanism and adjusts the processing strategy dynamically by producing early feedback, releasing unnecessary system resources and terminating corresponding pattern monitor. Task II - Complex Event Pattern Detection over Streams with Out-of-Order Data Arrival. In this task, a mechanism to address the problem of processing event queries specified over streams that may contain out-of-order data is studied, which provides new physical implementation strategies for the core stream algebra operators such as sequence scan, pattern construction and negation filtering. Task III - Complex Event Pattern Detection over Streams with Interval-Based Temporal Semantics. In this task, an expressive language to represent the required temporal patterns among streaming interval events is introduced and the corresponding temporal operator ISEQ is designed