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Lifelog Which Can Provide a Holistic View of User Activity and Behavior

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Lifelog Which Can Provide a Holistic View of User Activity and Behavior Mining Mind Master Presentation Contents MM V3.0 Contents / 2 Data CurationLayer Information CurationLayer Knowledge Curation Layer Service CurationLayer Supporting Layer Data Curation Layer (DCL) Curating Multimodal Sensory data for Health and Wellness Platforms Background / 4 • The focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices • Healthcare providers are focusing on when, where, and how; care and support are delivered to the particular patient and service consumer • Most prevalent diseases are partly caused or aggravated by poor lifestyle choices that people make in their daily routine Image source : http://www.southcoasthealthandwellness.com/integrative-medicine/ Motivation for Data Curation / 5 • Advent of smart and personal devices • Healthcare providers want to empower people to take care of their health and wellness by providing them with timely, ubiquitous, and personalized support • Most current solutions are single-device focused and have a limited scope • Unable to generate a context-rich user lifelog which can provide a holistic view of user activity and behavior • A context-rich lifelog is also a low-cost way to acquire valuable user information on which effective interventions from healthcare professionals can be based Image source: http://mobihealthnews.com/17827/docomo-omron-healthcare-launch-connected-health-venture-in-japan Data Curation as a Framework (DCF) / 6 Responsibilities 1. Device Independent sensory data acquisition 2. Curation of context-rich user lifelog ▪ Acquisition of multimodal raw sensory data in real-time 3. Monitoring of user lifelog for push-based interventions ▪ Synchronization of multimodal raw sensory data in a distributed 4. Support for the evolution and re-usability of sensory data ▪ Preparation of data instances for context 5. Integrated as a core foundation to health and wellness platforms determination Sensory Sensory Context Data Data Sensory Instance Acquisition Raw Data Synchronizer Data Writer Service Buffer Queue ▪ Curation and persistence of user context Data Acquisition and Synchronization Query Loader Data Data Exporter in the form of user lifelog Data Data Lifelog Curation Lifelog Persistence Retrieval Model Service ▪ CRUD operations for user lifelog Data Format Lifelog Representation and Mapping User Active Data evolution Profiles Reader Situation Monitor Event Constraint ▪ Non-volatile persistence of raw sensory Event Configurator Configurator LLM Data Writer Configurator Config. Data Data data and user lifelog in a Big Data Contract Lifelog Monitor Intermediate environment ▪ Monitoring of user lifelong for situations Database Data Data Acquisition and Curation Persistence to respond ▪ Active interface to Big Data for ▪ Hosting and execution of static Query Generator visualization and analytics Data Schema situations Exporter ▪ Passive interface to Big Data for data Scan Service HDFS HIVE driven knowledge generation ▪ Incorporation and execution of dynamic Passive Data Reader Physical Data Storage situations Non-Volatile Sensory Data Persistence Data Curation Framework DCF as a Cloud Implementation / 7 • Ubiquitous nature of the cloud provides DCF the ability to acquire sensory data in different contexts and environments • The cloud provides a central yet scalable computational resource that can accumulate sensory data from clients without being concerned with their computational abilities • The cloud provides a hub for context curation and monitoring for anomalies detection • To support the volume of data accumulated by DCF, the cloud provides a big data platform Data Curation from Framework to Platform / 8 • Data Curation Framework has been adopted as Service API the foundation for Mining Minds platform as Service Generation Supporting independent layer called Data Curation Layer or Recommendation Recommendation Service Layer DCL Manager Interpreter Orchastrator • Responsibilities of DCL are directly aligned with Knowledge Creation and Management UI / UX the requirements of Mining Minds Platform Data-Driven Expert-Driven Knowledgebase Security and Privacy Sensory Sensory Context Data Context Acquisition Data Sensory Instance Acquisition Raw Data Synchronizer Data Writer Service Buffer Queue Query Loader Feedback Data Acquisition and Synchronization High Level Context-Awareness Analysis Data Data Exporter Data Data Lifelog Curation Persistence Retrieval Model Lifelog Service Low Level Context-Awareness Data Format Lifelog Representation and Mapping User Active Data Profiles Reader Situation Monitor Event Constraint Event Big Data Storage and Processing Descriptive Configurator Configurator LLM Data Writer Configurator Config. Data Data Analytics Contract Sensory Data Processing and Persistence Lifelog Monitor Intermediate Database Data Data Acquisition and Curation Persistence Mining Minds Big Data Storage Gateway Query Generator Data Schema Exporter Scan Service HDFS HIVE Passive Data Reader Physical Data Storage Multimodal Data Source Non-Volatile Sensory Data Persistence Data Curation Framework DCL High Level Architecture / 9 • Data Curation Layer (DCL) is currently in its 5th iteration for Mining Minds Version 3.x DCL Execution Flow in Mining Minds Platform / 10 Supporting Layer Service Curation Layer Knowledge Curation Layer Information Curation Layer Data Curation Layer DCL Service Data Acquisition and Instance Sensory Data Raw Content Buffers Synchronization Context EP. 7 Writer Reader Acquisition Service Sensory Recv. Data Send for Send Sensory Data Synchronizer EP. 6 Request/ EP. 1 Queue Context Recv. Response Scan Enqueue EP. 2 Dequeue Life-log Representation and EP. 3 Mapping Context Mapper Recv. Create Instance Send for Persistence Life-log Mapper Model Create Data Writer ORM Layer Data Situation Writer Create EP. 4 Configurator CRUD Read Create EP. 5 Create Raw Data Constraints Recv. Life-log Backup Storage Service Intermediate Configurator Database Create Video Data Storage Distributed Data Persistence Service Big Data Query Storage Create Loader Scan Trigger Active Scan Data Hive Passive Situation Event Detector Query Data Create Data Queries Select Data Create Reader Loader Exporter Response Schema Cache Exporter Response Reader Life-log Monitoring Online Process Offline Process Related Work / 11 Contributions Limitations Insight1 • An energy efficient continuous sensing framework • Limited devices • Uses wearable devices with small data footprint • Smaller Data footprint • Opportunistic (Event and interval driven) sensing • Does not take computational complexity of process in account NetBio2 • It assembles vast amounts of curated and annotated, • Only supports data repositories as data clinical and molecular data sources • Big data technology for permanent persistence and core • Support for Clinical domain only logic layers to make correlations between the billions of • Offline and Batch processing data points • No real-time physical sensor based • A rich set of APIs that enable clients to integrate their continuous sensing workflows and scenarios SAMI3 • Data-driven Development (D3) platform for receiving, • Data exchange-centric Implementation storing and sending data to/from IoT devices. • No effective processing on accumulated • Any device can send data in various formats which is then data normalized into a JSON format and stored in the cloud • No curation mechanism for data representation DCL vs. State-of-the-art / 12 • DCL is a novel attempt to implement a raw sensory data acquisition, curation, and monitoring over cloud platform • The sensory data acquisition services of DCL are independent of data sources. • With scalability in mind, numerous multimodal data sources can communicate in parallel, making it a more IoT-oriented implementation • DCL considers all of the communicating devices as a source of raw sensory data; thus, generating a context- rich user lifelog • DCL implements situation detection on lifelog instances based upon expert-driven rules in correlation with user profiles, keeping the monitoring vigilant as well as personalized • The computation over the accumulated data and lifelog is performed over a cloud platform, keeping the framework compatible with data source with low computational abilities • From an evolutionary perspective, complex computational algorithms for context identification, data fusion, and mining can be implemented without disturbing client implementations Distributed Data Storage DCL Distributed Data Storage Terminologies / 14 • Terminologies • Life-log Data • Intermediate Data • Raw Sensory Data • Big Data • Personalized Big Data DCL Personalized Big Data Deployment / 15 Personalized Big Data Intermediate Data Relational Database + + (SQL Server) } Life-log User Profile Life-log Monitoring Technology Physical Location Service Provider Big Data + + Big Data Store (Hadoop) } RAW Sensory 3D Video Life-log (historic) Technology Physical Location Service Provider Data Acquisition & Synchronization (DAS) Overview / 17 • Acquisition of heterogeneous data from Multi- modal data sources in Real-time is a must for data curation layer • This acquisition of data must be dynamic, parallel and of high performance to support the influx of multimodal data at real-time • For reliable acquisition of data at real-time, sensory data must be synchronized with resolution to the distributed clock issues Motivation / 18 • Sensory data is generated after every 3 seconds from data sources • Real-time data acquisition • Mining Minds Platform is using data from multiple data
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