Sunday, November 19, 2017

Xanadu Based Big Data Deep Learning for Medical Data Analysis


Part I
Deep Learning for Medical Data Analysis Introduction
Automated Skin Cancer Classification
Automated Diabetic Retinopathy Classification
Brain Tumor Research
Alzheime Prediction
A Survey on Medical Image Deep Learning Research
Cardiac Arrhthymia Detection
ICU Patient Care

Part II
Deep Learning Introduction
Convolution Process Details
Issues with Big Data Deep Learning
Distributed Deep Learning for Medical Big Data Analysis
Challenges of Deep Learning for Medical Data Analysis
Content Based Image Retrieval (CBIR)

Part III
Xanadu Functionality
Xanadu Commodity Storage System Use Case
Xanadu Cloud Computing Use Case
Xanadu + Deep Learning + Hadoop + Spark Integration
Xanadu based Big Data Deep Learning System for Medical Data Analysis
Xanadu CBIR Demo


Friday, October 27, 2017

(Seminar) Xanadu Big Data Deep Learning System for Medical Data Analysis

의료 빅데이터 딥러닝 시스템 및 빅데이터 센터 심포지움
일시: 2017년 11월 9일(목요일), 오전 10:00 ~ 12:00
장소: 중앙보훈병원 중앙관 지하2층 대강당 (
10.00 ~ 10.50
제나두 기반 의료 빅데이터 딥러닝 시스템
이근호 박사 (미국 제나두 빅데이터 대표)
10.50 ~ 11.05
홍익대학교 빅데이터 센터 소개
표창우 교수 (홍익대학교 공대학장)
11.05 ~ 11.30
Pub/Sub 기반 이종 의료 정보 실시간 패턴 분석 (CEP) 및 전파
윤영 교수 (홍익대학교 컴퓨터공학과)
11.30 ~ 12.00
패널토론: 제나두 기반 의료 빅데이터 딥러닝 시스템 구축 및 상호협력 연구 프로젝트 추진 방안
사회: 김억 교수 (홍익대학교 빅데이터 센터)
패널: 이근호 박사, 표창우 교수, 윤영 교수, 보훈병원 김봉석 기조실장 및 관계자들

Monday, September 25, 2017

Fourth Industrial Revolution & Xanadu: Big Data + IoT + Deep Learning Integration Strategy

Part I
The Fourth Industrial Revolution?
Big Data Introduction
Big Data Analysis Flow
Big Data Use Cases

Part II
Big Data Use Cases
IoT Introduction
IoT Use Cases

Part III
IoT Use Cases
Artificial Intelligence Overview
Deep Learning Introduction
Deep Learning Use Cases

Part IV
Deep Learning Use Cases
Big Data in IoT & Deep Learning
Challenges of IoT Big Data Analytics Applications
Distributed Deep Learning
Xanadu Functionality
Xanadu Performance BMT
Xanadu Fault Tolerance Test Demo
Xanadu Use Cases
Xanadu Commodity Storage System Use Case
Xanadu Cloud Computing Use Case

Part V
Xanadu Cloud Computing Use Case
Xanadu + Deep Learning + Spark + Hadoop Integration
Xanadu based Big Data Deep Learning System

Monday, July 10, 2017

Xanadu for Big Data + IoT + Deep Learning + Cloud Integration Strategy (YouTube Presentation Video)

Silicon Valley Xanadu Promotional Event Presentation Part I
Big Data in IoT & Deep Learning
Challenges of IoT Big Data Analytics Applications
Challenges of Cloud-based IoT Platform
Cloud-based IoT Platform Use Case: GE Predix for Smart Building Energy Management

Silicon Valley Xanadu Promotional Event Presentation Part II
Fog/Edge Computing & Micro Data Centers
Deep Learning for IoT Big Data Analytics Introduction
Deep Learning for IoT Big Data Analytics Use Case
Distributed Deep Learning

Silicon Valley Xanadu Promotional Event Presentation Part III
Big Data + IoT + Cloud + Deep Learning Insights from Patents
Big Data + IoT + Cloud + Deep Learning Strategy Development

Silicon Valley Xanadu Promotional Event Presentation Part IV
Designing Data-Intensive Applications
Xanadu Functionality
Xanadu Use Case
Xanadu + Deep Learning + Hadoop Integration

Part I+ II + III + IV Presentation Slide

Friday, June 30, 2017

Xanadu for Big Data + Deep Learning + Cloud + IoT Integration Strategy Presentation


Monday, June 12, 2017

Xanadu for Big Data + Deep Learning + Cloud + IoT Integration Strategy

Event Description:
Alex G. Lee, a managing partner of Xanadu Big Data, LLC, will talk about Xanadu technology and use cases for Big Data + Deep Learning + Cloud + IoT Integration Strategy.

Xanadu is the most advanced big data management platform technology that is developed to take care of the requirement of high speed processing of diverse type of high volume data. Xanadu can provide a massively scalable fault tolerance system that can connect multiple storages. Xanadu can provide high throughput and low latency data management system. Xanadu provides ACID compliance data management system. Xanadu is designed to be a composable architecture in order to be selected and integrated with other big data system elements such as IT infrastructures and data analytics to satisfy specific big data use requirements. Xanadu is designed for the heaviest workloads that can supports concurrent queries without conflict. For example, Xanadu can support thousands of sensors accessing and updating data at the same time. Thus, Xanadu enables real-time IoT analytics for industrial IoT applications. Xanadu also can support data-intensive distributed deep learning applications involving massive volume multimedia data.

Please join to meet Alex G. Lee for lunch and introduction of Xanadu.

Date: 6/29/2017

Time: 11.30 am – 3 pm

Location: DLA Piper in Palo Alto, 2000 University Ave, Palo Alto, CA 94303

11.30 am – 12.00 pm Check-in
12.00 pm – 1.00 pm Lunch & Networking
1.00 pm – 1.10 pm Introduction by DLA Piper
1.10 pm – 2.30 pm Presentation by Alex G. Lee
2.30 pm – 3.00 pm Networking
3.00 pm Meeting adjourn

This event is by invitation only. If you want to attend the event please send RSVP to Alex G. Lee ( with your name, company name, title and email address.

Friday, May 19, 2017

Xanadu Cloud Computing Use Case: Protection of PCs from Ransomware

Xanadu Cloud Computing Use Case
Demo: Daeil Foreign Language High School, S. Korea

Xanadu is a key-value NoSQL big data management platform technology that provides fault tolerant ACID property and high throughput/low latency with massive scalability. Xanadu is designed for the heaviest workloads, and supports support concurrent queries without conflict.

Xanadu can be exploited for the back-end storage technology that allows remote client computers can access data and computing/application resources via the standard iSCSI network protocol. With iSCSI supported natively by any operating system, Xanadu makes it easy to securely store and access data from any machine on the network. Xanadu also can be used for providing services that allow remote computer systems to “boot” from a stored system drive image. With diskless units on users’ desks and all data (including the operating system disk) remains in the secure cloud servers, administrators are free to deploy diskless PCs to the desktop with their inherent advantages of higher data security, quicker disaster recovery, smaller office footprint and better energy consumption.

In-built deduplication functionality of Xanadu enable saving of cloud data storage resources a lot. For example, Xanadu can store thousands of 25GB basic Windows 7 disk images in only a few hundred gigabytes of actual storage. Xanadu, therefore, enables a simple and highly efficient means to centrally manage cloud data storage, particularly for standardised PC installations that need to be booted almost identically in many places. Time stamping functionality of Xanadu also offers an efficient snapshot capability that enables users to “reset” their stores to a previous saved “good” state. Especially, this resetting capability will be a good solution for proving protection of client PCs from malwares including notorious Ransomwares.

Xanadu for Protection of PCs from Ransomware.

Contact: Alex G. Lee (

Monday, March 20, 2017

IoT Big Data Analytics Insights from Patents

IoT (Internet of things) big data analytics is becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big data analytics system is Hadoop, an open-source software framework that supports data-intensive distributed applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the architectural framework MapReduce, collects both structured data and unstructured data, processes the collected data set in a distributed network cluster in parallel, and extracts valuable information from the processed data set within a short time.

IBM patent application US20160070816 illustrates a system for processing large scale unstructured data in real -time. The interconnected IoT sensing devices continuously generate massive information at a very high speed. Thus a technology for effectively processing a huge amount of information in the form of a data stream in real-time is very important. The real time big data analysis system includes a receiver for receiving streamed input data from live data sources, a pattern generator for deriving emergent patterns in data subsets, a pattern identifier for identifying a repeating pattern and corresponding data subset within the emergent patterns, a compressor for reducing the identified data subset and identified pattern to a compressed signature and a repository for storing the streamed input data with the compressed signature and without the identified data subset in which the data subset can be rebuilt if necessary using the compressed signature.

In IoT, millions of events often generated from IoT sensors and devices. Thus, it is very important to develop real-time data streaming and processing systems for IoT analytics. There are several open-source real-time data streaming and processing systems are available including Apache Kafka, Storm, and Spark. Most of open-source real-time data streaming and processing systems offer default schedulers that evenly distribute processing tasks between the available computation resources. However, such schedulers are not cost effective because substantial computation resources are lost during assignment and re-assignment of tasks to the correct sequence of computation resources in the stream processing system, which results in significant latency in the system., inc. patent application US20170075693 illustrates a cost effective improved real-time data streaming and processing systems for IoT analytics.

In Industrial IoT (IIoT) applications (e.g., manufacturing, oil and gas, mining, transportation, power and water, renewable energy, heath care, retail, smart buildings, smart cities, and connected vehicles), it is not practical to send all of that data from sensors embedded in industrial machines to cloud storage because connectivity not enough bandwidth in a cost effective way and difficulty in practical implementation of effective real-time decision making and prediction systems. FogHorn Systems, Inc. patent application US20170060574 illustrates a real-time edge IoT analytics system (e.g., Fog Computing) that can handle the large amounts of data generated by industrial machines and provides intelligent edge computing platform.

Data monetization is a business model to generate revenue from available data sources or real time streamed data by instituting the discovery, capture, storage, analysis, dissemination, and use of the data. Data monetization leverages data generated through business operations as well as data associated with individual actors and with electronic devices and sensors participating in a given network. IoT can facilitate generating location data and other data from sensors and mobile devices. Big data system enables identification, analysis, selection and capitalization of the IoT data monetization opportunities. Data monetization value chain includes the data producers, data aggregators, data distributors and data consumers. Data as a service (DaaS) models for transactions involving big data can be possible.

mFrontiers, LLC patent application US20160050279 illustrate a system for operating the IoT big data analysis service for data monetization. The system analyzes the stored data from the IoT devices in the cloud and produces a big data analysis report. A client can purchase or sell information on the analyzed big data analysis report through the virtual big data marketplace. The big data analysis report can include information on a preliminary analysis report whose results vary according to analysis time or period. When a third party analyzes big data using the information on preliminary analysis report, a writer who uploaded the information on preliminary analysis report to the analysis report marketplace charges fees for the information.

New Technologies & Associates, Inc. patent application US20150179079 illustrates a healthcare IoT big data analytics system for real time monitoring of a patient's cognitive response to a stimulus. The big data analysis of massive data obtained by the sensing devices can provide many value-added healthcare services. The real time monitoring system includes a mobile or tablet device, a user interface disposed on the mobile device, sensors monitoring user interaction with the mobile device and capturing kinesthetic and cognitive data. The real time monitoring system also includes a processor for comparing the kinesthetic and cognitive data and comparing the data to a baseline, and identifying relative improvement and impairment of cognition skills from the comparison exploiting big data analytics.

 ©2017TechIPm, LLC All Rights Reserved

Friday, March 17, 2017

LTE Standard Related Patents Landscape 1Q 2017

By exploiting a big data patent search and analysis tool - the IPlytics Platform (Ref., TechIPm, LLC ( extends its more than 6 years of custom research of LTE patents to include a large number of new candidates for the LTE standard essential patents (SEPs). The IPlytics Platform data sources cover over 80 million patent documents for 98 worldwide countries, over 60 million scientific articles, information regarding over 3 million companies, about 2 million standards documents from 96 standard setting organizations including over 200,000 SEPs declared at the major standard setting organizations (SSOs).

LTE issued patents for the LTE UE (cellular phones, smart phones, PDAs, mobile PCs, etc.), base station (eNB) products, and other RAN (Radio Access Network) related products are searched in the USPTO as of 1Q 2017.  The searched patents are further reviewed with respect to 3GPP Release 10 technical specifications (LTE-Advanced) for LTE RAN technical specifications: PHY: TS 36.101, 211, 212, 213, 305; L2/L3 Protocols: TS 36.300, 304, 321, 322, 323, 331, 355 and 3GPP Release 11 technical specifications for CoMP (TR 36.819), 3GPP Release 12 technical specifications for EPDCCH (TS 36.211, 213) and D2D (Device to Device; TR 36.843) Communications.
The identified LTE standard related patents cover not only ETSI declared SEPs but also candidates for SEPs that were not declared. Nearly 4000 US issued patents are finally selected as the LTE standard related patents. The identified LTE standard related patents are also updated for the current assignees.

The identified LTE standard related patents are further categorize through the evaluation process by technologies for implementations of the LTE baseband modem ( OFDM/OFDMA (Frame & Slot Structure, Modulation), SC-FDMA (PUSCH, PUCCH), Channel Estimation (UL RS, DL RS, CQI), Cell Search & Connection (PRACH, DL SS), MIMO (Transmit Diversity, Spatial Multiplexing), Resource Management (Resource Allocation, Scheduling), Coding (Convolution, Turbo), Power Control, HARQ, Carrier Aggregation, Relay, and Positioning Technology) and radio protocols (Random Access, HARQ, Channel Prioritization, Scheduling (Dynamic, SPS), Protocol Format (PDUs, SDUs), Radio Link Control (ARQ), PDCP Process (SRB, DRB, ROHC), Security (Ciphering, Integrity), System Information, Connection Control, Mobility (Handover, Inter-RAT, Measurements), QoS, MBMS, and Carrier Aggregation). To evaluate the essentiality of a LTE patent, patent disclosures in claims and detail description for each LTE related patent also are compared to the LTE technical specifications.

Leaders in LTE standard related patents IPR ownership as of 1Q 2107 are Qualcomm followed by InterDigital, Samsung Electronics, LG Electronics, Google, Ericsson, Nokia (+ Alcatel-Lucent), Apple, Panasonic, Optis Wireless Technology LLC, Intel, Huawei, NTT Docomo, ZTE, BlackBerry, Amazon, NEC, Texas Instruments, and ETRI. Here, most of Amazon’s LTE patents were acquired from LG Electronics.

For more information, please contact Alex Lee at .