data bag org
play

data-bag.org cloud-free file sync & backup Public domain - PowerPoint PPT Presentation

Jun 16, 2023 •474 likes •578 views

data-bag.org cloud-free file sync & backup Public domain weather images from http://openclipart.org/search/?query=weather by: Machovka, warszawianka, eady, and jgm104 Dangers of the cloud storage Cloud storage services abound &

  1. data-bag.org cloud-free file sync & backup Public domain weather images from http://openclipart.org/search/?query=weather by: Machovka, warszawianka, eady, and jgm104

  2. Dangers of the cloud storage ● Cloud storage services abound & free, but ● Can you trust them? – not to harvest & analyze – not to sell or share – not to leak ● your – financial documents – health records – other private files? data-bag.org: cloud-free file sync & backup

  3. Sensitive records: requirements ● Security and control ● Reliable storage ● Simple access procedures ● Safety net against – equipment failures – human errors (including those discovered late) – haste and confusion data-bag.org: cloud-free file sync & backup

  4. Technical goals for data-bag ● Open-source: transparency, flexibility, reuse ● Portable – store and recover files on most machines – no need to install, run from the medium if needed ● Efficient – works with small media: ● flash cards, USB sticks – no need for frequent purges ● Fast data-bag.org: cloud-free file sync & backup

  5. data-bag now ● A Java application – runs from a single ~1.5Mb .jar file – runs anywhere with a Java runtime – supports OpenJDK ● Command-line interface ● Set up once, sync quickly ● Recovery, undo, conflict resolution ● Storage ( bag ) compression & encryption data-bag.org: cloud-free file sync & backup

  6. data-bag in action ● See yourself at www.data-bag.org ● Download the binary by clicking Download ● Install Java SDK & Apache ANT to build from source – sudo apt-get install openjdk-6-jdk ant - or - – http://java.oracle.com/ – http://ant.apache.org/ ● Read doc/manual.md or doc/manual.html for instructions data-bag.org: cloud-free file sync & backup

  7. data-bag is free ● Project's code is covered by – GNU Affero GPL v.3 with amendments ● Code reused from Apache ANT is covered by Apache License v.2.0 ● Libraries are covered by – Commons: Apache License v.2.0 – H2: Eclipse Public License v.1.0 data-bag.org: cloud-free file sync & backup

  8. The future of data-bag ● rsync integration: sync and store on a server ● web-interface: easier to use, server access, mobile-ready ● Android port? ● core improvements: – selectivity – digests vs. dates – renaming/moving files; etc. ● testing and documentation data-bag.org: cloud-free file sync & backup

  9. data-bag and you ● You are welcome to use it ● Your feedback matters ● Skills you can contribute: – algorithms & data mgmt. with Java & SQL (core) – build a web-interface in Java (web) – reading and writing C/C++ with Linux runtime (rsync) – building Android apps with native GUI ● Dual-license contributions: AGPL & Apache data-bag.org: cloud-free file sync & backup

  10. Contacting the data-bag team ● Download at www.data-bag.org ● Join https://github.com/data-bag/code ● Check out www.livitski.name ● E-mail data-bag at livitski dot name ● Ask questions now and later Thank you! Stan. data-bag.org: cloud-free file sync & backup

Recommend

Data Preparation Data cleaning Data integration and transformation (Data

Data Preparation Data cleaning Data integration and transformation (Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Data integration and transformation (Data preprocessing) Data reduction, Feature selection Discretization and concept hierarchy generation 2

891 views • 21 slides
DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data mining is the use of efficient techniques for the analysis of very large collections of data and the extraction of useful and possibly unexpected

2.4k views • 94 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 68 / 91 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4 2

579 views • 8 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 28 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

455 views • 23 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 51 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

344 views • 17 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 68 / 91 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4 2

729 views • 24 slides
Data Preparation Data cleaning Discretization (Data preprocessing) Data

Data Preparation Data cleaning Discretization (Data preprocessing) Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Discretization (Data preprocessing) Data integration and transformation Data reduction, Feature selection 2 Why Prepare Data? Why Prepare

462 views • 15 slides
DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and repairing account for about 60% of the work of data scientists." Christian Kaestner Required reading: Schelter, S., Lange, D., Schmidt, P.,

1.1k views • 86 slides
Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science dense complex static/non-interactive often not accompanied by an easy-to-follow narrative Data art beautiful abstract

647 views • 5 slides
Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by some geophysical device Most surveys are comprised of linear traverses or transects Typically constant data spacing Perpendicular to

432 views • 12 slides
Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges: data Data type: numerical sensor readings. Rich and massive data, spatially distributed and correlated. Data dynamics: data

714 views • 48 slides
Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

DATA Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data Obtaining data Further study THE ROLE OF DATA Data refers to observed facts there are 82 persons in this train the weight of

683 views • 22 slides
DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type system sets the stage for the capabilities of the language Understanding data types empowers you as a data scientist DataCamp Data Types for Data

527 views • 23 slides
q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the new currency of the digital world . World Economic Forum 1 Problem: access to big data Data accessed & analyzed Data in organizations

543 views • 30 slides
Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

CLEANING DATA IN PYTHON Concatenating data Cleaning Data in Python Combining data Data may not always come in 1 huge file 5 million row dataset may be broken into 5 separate datasets Easier to store and share May have

426 views • 26 slides
Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What data do I use most? Session Target: After todays session, you will be able to: 1. Insert/include data opportunities into PL models. 2.

710 views • 12 slides
Data Preparation Discretization Data cleaning (Data pre-processing) Data

Data Preparation Discretization Data cleaning (Data pre-processing) Data

Data Preparation Why prepare the data? Data Preparation Discretization Data cleaning (Data pre-processing) Data integration and transformation Data reduction, Feature selection 2 Why Prepare Data? Why Prepare Data?

426 views • 17 slides
Introduction to IoT data AN ALYZ IN G IOT DATA IN P YTH ON Matthias Voppichler IT Developer

Introduction to IoT data AN ALYZ IN G IOT DATA IN P YTH ON Matthias Voppichler IT Developer

Introduction to IoT data AN ALYZ IN G IOT DATA IN P YTH ON Matthias Voppichler IT Developer Course overview Collect and analyze IoT data Gather data API Endpoints Data Streams Visualize data Combine datasets Detect patterns ML Model

756 views • 27 slides
Modeling Data the different views on Data Mining Views on Data Mining Fitting the data

Modeling Data the different views on Data Mining Views on Data Mining Fitting the data

Modeling Data the different views on Data Mining Views on Data Mining Fitting the data Density Estimation Learning being able to perform a task more accurately than before Prediction use the data to predict future data

517 views • 29 slides
Abstract Data Types & Templates 1 Abstract Data Type 2 Data and Abstraction

Abstract Data Types & Templates 1 Abstract Data Type 2 Data and Abstraction

Abstract Data Types & Templates 1 Abstract Data Type 2 Data and Abstraction Operations on data are central to most solutions Think abstractly about data and its management Typically need to Organize data Add data Remove

756 views • 30 slides
Tidy data CLEAN IN G DATA IN P YTH ON Daniel Chen Instructor Tidy data Tidy Data paper

Tidy data CLEAN IN G DATA IN P YTH ON Daniel Chen Instructor Tidy data Tidy Data paper

Tidy data CLEAN IN G DATA IN P YTH ON Daniel Chen Instructor Tidy data Tidy Data paper by Hadley Wickham, PhD Formalize the way we describe the shape of data Gives us a goal when formatting our data Standard way to organize data

462 views • 27 slides
Big Data Analytics Armistead Boyd SVP, Product & Data Partnerships October 25, 2016 What is

Big Data Analytics Armistead Boyd SVP, Product & Data Partnerships October 25, 2016 What is

Big Data Analytics Armistead Boyd SVP, Product & Data Partnerships October 25, 2016 What is Big Data Analytics? Data Future Data Past Data Present 1 Big Data Trends Data Driven Organization Other Executives 5% 8% Operations Data

620 views • 22 slides
Concatenating data CLEAN IN G DATA IN P YTH ON Daniel Chen Instructor Combining data Data may

Concatenating data CLEAN IN G DATA IN P YTH ON Daniel Chen Instructor Combining data Data may

Concatenating data CLEAN IN G DATA IN P YTH ON Daniel Chen Instructor Combining data Data may not always come in 1 huge le 5 million row dataset may be broken into 5 separate datasets Easier to store and share May have new data for each

555 views • 31 slides
Data Collection and HIVe Current Data Collection For those collecting data, you are use to

Data Collection and HIVe Current Data Collection For those collecting data, you are use to

Data Collection and HIVe Current Data Collection For those collecting data, you are use to the Excel data collection sheets All Ryan White Funded entities will be required to report client level data Non-Medical Case Management

266 views • 13 slides

More recommend