OpenStack is an open-source cloud computing platform that consists of a series of related projects that control compute, storage, and networking resources throughout a datacenter. It is designed to scale to very large deployments across multiple datacenters and uses open APIs that are controlled by an open-source community. The core components include OpenStack Compute for provisioning and managing large networks of virtual machines, OpenStack Image Service for managing machine images, and OpenStack Object Storage for storing and retrieving large amounts of unstructured data objects.
This talk describes our experiences from hosting scientific research application in the Microsoft Cloud. Covers an overview of Microsoft Azure capabilities, examples of big data analysis for science, data collections, science gateways and science virtual machine libraries.
Searchlight offers eDiscovery services such as collecting, searching, and reviewing evidentiary material to generate document productions. Their services allow clients to review documents in their native format without altering metadata. Searchlight can collect data from various sources, cull and search the data using advanced tools, and support native file review to reduce review time and costs. They also offer forensic services like recovering deleted files and imaging damaged drives.
This document discusses provenance standards and information. It covers:
- Why provenance is important for reproducibility in science. Provenance tracks how data was produced and versions of software/tools used.
- Current provenance standards include PROV, which introduced a provenance data model and ontology for describing the provenance of data.
- Docker can contain some provenance information and allow distributing software and data while tracking versions. Provenance information needs to be kept up-to-date for data, tools, and workflows as they change over time.
- Challenges include tracking provenance of distributed Docker images and transmitting provenance between repositories and linked open data formats.
This talk explains how cloud computing to speed up your research, and provide capabilities that are otherwise out of reach. Big data, data science, machine learning, high-performance computing are all available on-demand using Microsoft Azure.
Researchers around the world can apply for free cloud computing time for their projects at www.Azure4Research.com
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon UniversityNodejsFoundation
Today, more data is accumulated than ever before. It has been estimated that over 80% of data collected by businesses is unstructured, mostly in the form of free text. The statistical community has developed many tools for analysing textual data, both in the areas of exploratory data analysis (e.g. clustering methods) and predictive analytics. In this talk, Philipp Burckhardt will discuss tools and libraries that you can use today to perform text mining with Node.js. Creative strategies to overcome the limitations of the V8 engine in the areas of high-performance and memory-intensive computing will be discussed. You will be introduced to how you can use Node.js streams to analyse text in real-time, how to leverage native add-ons for performance-intensive code and how to build command-line interfaces to process text directly from the terminal.
A graph database is a database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. It represents data in graph structures with nodes, edges and properties, allowing for flexible modeling and querying of highly connected datasets. Graph databases are well suited for applications with complex relationships that are difficult to represent in traditional relational databases.
A presentation at the NIH Workshop on Advanced Networking for Data-Intensive Biomedical Research. The talk covers our work with the science community on using cloud computing to enhance and improve basic research for data analysis and scientific discovery
OpenStack is an open-source cloud computing platform that consists of a series of related projects that control compute, storage, and networking resources throughout a datacenter. It is designed to scale to very large deployments across multiple datacenters and uses open APIs that are controlled by an open-source community. The core components include OpenStack Compute for provisioning and managing large networks of virtual machines, OpenStack Image Service for managing machine images, and OpenStack Object Storage for storing and retrieving large amounts of unstructured data objects.
This talk describes our experiences from hosting scientific research application in the Microsoft Cloud. Covers an overview of Microsoft Azure capabilities, examples of big data analysis for science, data collections, science gateways and science virtual machine libraries.
Searchlight offers eDiscovery services such as collecting, searching, and reviewing evidentiary material to generate document productions. Their services allow clients to review documents in their native format without altering metadata. Searchlight can collect data from various sources, cull and search the data using advanced tools, and support native file review to reduce review time and costs. They also offer forensic services like recovering deleted files and imaging damaged drives.
This document discusses provenance standards and information. It covers:
- Why provenance is important for reproducibility in science. Provenance tracks how data was produced and versions of software/tools used.
- Current provenance standards include PROV, which introduced a provenance data model and ontology for describing the provenance of data.
- Docker can contain some provenance information and allow distributing software and data while tracking versions. Provenance information needs to be kept up-to-date for data, tools, and workflows as they change over time.
- Challenges include tracking provenance of distributed Docker images and transmitting provenance between repositories and linked open data formats.
This talk explains how cloud computing to speed up your research, and provide capabilities that are otherwise out of reach. Big data, data science, machine learning, high-performance computing are all available on-demand using Microsoft Azure.
Researchers around the world can apply for free cloud computing time for their projects at www.Azure4Research.com
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon UniversityNodejsFoundation
Today, more data is accumulated than ever before. It has been estimated that over 80% of data collected by businesses is unstructured, mostly in the form of free text. The statistical community has developed many tools for analysing textual data, both in the areas of exploratory data analysis (e.g. clustering methods) and predictive analytics. In this talk, Philipp Burckhardt will discuss tools and libraries that you can use today to perform text mining with Node.js. Creative strategies to overcome the limitations of the V8 engine in the areas of high-performance and memory-intensive computing will be discussed. You will be introduced to how you can use Node.js streams to analyse text in real-time, how to leverage native add-ons for performance-intensive code and how to build command-line interfaces to process text directly from the terminal.
A graph database is a database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. It represents data in graph structures with nodes, edges and properties, allowing for flexible modeling and querying of highly connected datasets. Graph databases are well suited for applications with complex relationships that are difficult to represent in traditional relational databases.
A presentation at the NIH Workshop on Advanced Networking for Data-Intensive Biomedical Research. The talk covers our work with the science community on using cloud computing to enhance and improve basic research for data analysis and scientific discovery
HDF Explorer is a data visualization program that reads HDF and HDF5 file formats. It allows users to browse data hierarchically and visualize it numerically in grids, as pixel colors, or as vector data by combining two scalar datasets. The software has over 4000 users in 54 countries, including government agencies, national labs, companies, and universities. A demo of HDF Explorer opening sample data files is included.
This document discusses file structures and their design. It introduces key concepts in file structure design like representing data on disk versus RAM. It then surveys the history of major file structure designs like sequential files, B-trees, and hashing. It describes developing a conceptual toolkit to understand fundamental file concepts and generic operations. It also discusses how to create an object-oriented toolkit to make file structures usable by implementing them as classes. Finally, it provides an overview of using objects in C++ for file structure design through features like classes, constructors, and operator overloading.
This document provides an overview of Bionimbus and the Open Cloud Consortium (OCC). Bionimbus is an open source cloud for biomedical research that provides services like elastic computing, databases, data transport and analysis pipelines. The OCC operates open clouds and develops standards to bridge private and public clouds. It runs an Open Cloud Testbed and is working to build an Open Science Data Cloud. The OCC aims to develop interoperable cloud architectures and operate infrastructure at data center scale to support open science.
This document summarizes information about the Intelligent Database Systems Lab at KAIST, including its members, activities, and research interests. The lab is led by Professor Hyun Soon Joo and includes 3 PhD students, 3 MS students, and 2 foreign interns. Their research focuses on context-aware computing, social-aware computing, sensor databases for wireless sensor networks, and healthcare applications using sensors. They have annual events like a home-coming day and winter workshop.
Exploration of multidimensional biomedical data in pub chem, Presented by Lia...Lucidworks (Archived)
The document discusses the development of a new search system for PubChem to allow for exploration of multidimensional biomedical data. The new system was needed to address the challenges of handling large and heterogeneous datasets with many relationships between data types in a way that allows for fast querying. The system leverages Apache SOLR to provide features like full text search, faceting, molecule structure searching and joining of related data. It includes backend components like SOLR, SQL and specialized search engines as well as web APIs and frontend interfaces like reusable widgets and a new search interface.
This document outlines a project between the Odum Institute and IQSS Dataverse team to integrate the Dataverse data repository system with iRODS, an open source data management system. The goals are to expand storage options for Dataverse, integrate curation workflows, and connect Dataverse to national research data infrastructure. A prototype will be developed to enable automated ingest of data from Dataverse to iRODS using rules and APIs. Challenges include migrating both systems to newer versions while maintaining authentication between them. An initial prototype is expected in August 2015.
Lessons Learned from a Year's Worth of Benchmarking Large Data Clouds (Robert...Robert Grossman
The document summarizes Sector, an open-source large data cloud computing platform, and compares it to Hadoop. Sector uses a file-based storage system instead of Hadoop's block-based HDFS, and features a more flexible UDF programming model compared to MapReduce. Benchmark results show Sector outperforming Hadoop on the Terasort and MalStone benchmarks, with speedups of up to 19x, due to its dataflow balancing, UDP-based transport, and other architectural advantages over Hadoop for data-intensive computing at scale. Lessons learned include the importance of data locality, load balancing, and fault tolerance in large-scale systems.
Accumulo Summit 2016: Accumulo in the EnterpriseAccumulo Summit
Many organizations are looking to Hadoop clusters in order to store and manage an ever-increasing amount of data. As the volume and variety of data in these systems grows, administrators are being confronted with more information, from more sources, than they have ever seen concentrated in a single place. The responsibility for securing all this data can be daunting to an administrator, even intimidating. Could the answer lie in Accumulo?
Conventional approaches to data security usually do not suffice for this scenario. They are often coarse-grained, applying only at the file or table level. In a world where arbitrary compute tasks can be pushed into the cluster, defining a security perimeter is difficult or impossible. On the other hand, relegating access policy enforcement to the application level instead of the database level ultimately invites a security disaster.
This is the world that Chief Security Officers, Chief Information Officers, and Chief Data Officers live in, and the problem of security for big data is the single biggest impediment to delivering a Hadoop-based solution in the enterprise’s production network. Numerous organizations have implemented Hadoop as a pilot, but find themselves blocked by similar considerations when the time to move into production:
• How do you implement fine-grained access controls in a Hadoop system?
• What about encryption at rest? Encryption in motion?
• How will this tie into our identity infrastructure?
• How will this fit into our operational workflow?
This keynote will explore the ways in which Apache Accumulo is uniquely positioned to mitigate or resolve problems around access control and security for big data, thus enabling Hadoop clusters to move from pilot to production.
– Speaker –
Russ Weeks
Software Architect, PHEMI Systems
Russ Weeks is a Software Architect at PHEMI. Prior to joining PHEMI Systems, Russ worked in the network management groups at Ericsson and Cray Supercomputers, where he discovered a passion for distributed data structures and algorithms. PHEMI Systems is a Vancouver, BC-based startup focused on the storage, retention and governance of structured and unstructured data.
— More Information —
For more information see http://www.accumulosummit.com/
This document provides an overview of Circos, a software package for visualizing data in circular form. It discusses Circos' installation process, file distribution, and generation of Circos plots. Circos allows researchers and data analysts to represent data at different levels in a circular layout and visualize network flows.
This document provides an overview of e-discovery and why it should be a priority for organizations. Key points include:
- E-discovery is the process of identifying, preserving, and producing electronic information for legal cases. It is important due to the large volume of electronic data and growing legal obligations.
- Getting the right e-discovery capabilities can help organizations respond more effectively to lawsuits, potentially avoid lawsuits, and reduce costs of email management and litigation.
- The document outlines important e-discovery lessons from past court cases and why data from multiple sources may need to be included in e-discovery processes.
IntellaCore is a company that helps other businesses become globally competitive without requiring significant capital investments. It does this by evaluating companies, helping them enter new international markets, and introducing them to new global vendors and suppliers. Case studies show how IntellaCore has helped companies expand into countries like Canada, the UK, and others by providing market intelligence and connecting them with potential partners. Business owners praise IntellaCore for providing measurable results that have helped grow their distribution and sales globally.
Delta InfraSuite is Delta Electronics' data center infrastructure solution that includes integrated power, cooling, rack, and management systems. It offers modular components that allow for scalable and efficient data center design. Key features include optimized power distribution and cooling, energy savings, easy installation and operation, and a centralized environment management system. Delta InfraSuite aims to help IT managers address the challenges of building and maintaining high-performance, eco-friendly data centers.
E Discovery General E Discovery Presentationjvanacour
This document provides an overview of key concepts and best practices regarding electronic discovery (e-discovery). It discusses the duty to preserve relevant evidence once litigation is reasonably anticipated. It also outlines the stages of managing e-discovery, including having a reasonable document retention policy prior to notice, issuing a litigation hold once notice is received, and complying with discovery requests once litigation begins. The document emphasizes communicating preservation obligations, overseeing preservation efforts, and producing electronic documents and metadata in a usable format.
This document provides an overview of electronic discovery (eDiscovery) including defining electronically stored information (ESI), understanding the eDiscovery process, and Hudson Legal's role. It explains that ESI includes emails, documents, social media posts and more. The eDiscovery process is broken down into the nine phases of the Electronic Discovery Reference Model (EDRM) including identification, preservation, collection, processing, review, analysis, production and presentation. Hudson Legal assists with project management, document review, and quality control during the review process.
Digital forensics involves analyzing digital artifacts like computers, storage devices, and network traffic as potential legal evidence. The process includes preparing investigators, collecting evidence while maintaining a chain of custody, examining and analyzing the data, and reporting the results. Key steps are imaging systems to obtain an exact duplicate without altering the original, recovering volatile data from memory, and using tools like EnCase and The Sleuth Kit to manually review and search the evidence for relevant information.
Computer forensics is a branch of digital forensic science involving the legal investigation and analysis of evidence found in computers and digital storage media. The objectives are to recover, analyze, and preserve digital evidence in a way that can be presented in a court of law, and to identify evidence and assess the identity and intent of perpetrators in a timely manner. Computer forensics techniques include acquiring, identifying, evaluating, and presenting digital evidence found in files, databases, audio/video files, websites, and other locations on computers, as well as analyzing deleted files, network activity, and detecting steganography.
Computer forensics is the “who, what, when, and how” of electronic evidence. Typically narrow in scope, it attempts to reconstruct events, focusing on the computer-based conduct of an individual or group of individuals. The types of cases involving computer forensics are numerous and varied – from the personal (i.e. locating hidden assets in a messy divorce case), to the political (i.e. investigating alleged misuse of government computers for political gain), to the dramatic (i.e. “What was your client’s former
employee downloading from the Internet before he was fired
and brought suit for wrongful termination?”).
This document provides an overview of computer forensics. It defines computer forensics as using analytical techniques to identify, collect, and examine digital evidence. The objective is usually to provide evidence of specific activities. Computer forensics is used for cases like employee internet abuse, data theft, fraud, and criminal investigations. The document outlines the history, approaches, tools, advantages, and disadvantages of computer forensics. It describes securing systems, recovering files, decrypting data, and documenting procedures used in investigations.
The presentation is all about computer forensics. the process , the tools and its features and some example scenarios.. It will give you a great insight into the computer forensics
Digital forensics is the preservation, identification, extraction and documentation of computer evidence for use in courts. There are various branches including network, firewall, database and mobile device forensics. Digital forensics helps solve cases of theft, fraud, hacking and viruses. Challenges include increased data storage, rapid technology changes and lack of physical evidence. Three case studies showed how digital forensics uncovered evidence through encrypted communications, text messages and diverted drug operations. The future of digital forensics includes more sophisticated tools and techniques to analyze large amounts of data.
This document provides an overview of computer forensics. It defines computer forensics as the process of preserving, identifying, extracting, documenting and interpreting computer data for legal evidence. The document outlines the history of the field from the 1970s to present day, describes the typical steps of acquisition, identification, evaluation and presentation, and discusses certifications, requirements, evidence collection, uses, advantages and disadvantages of computer forensics. It concludes that computer forensics is needed to uncover electronic evidence for prosecuting cybercrimes.
HDF Explorer is a data visualization program that reads HDF and HDF5 file formats. It allows users to browse data hierarchically and visualize it numerically in grids, as pixel colors, or as vector data by combining two scalar datasets. The software has over 4000 users in 54 countries, including government agencies, national labs, companies, and universities. A demo of HDF Explorer opening sample data files is included.
This document discusses file structures and their design. It introduces key concepts in file structure design like representing data on disk versus RAM. It then surveys the history of major file structure designs like sequential files, B-trees, and hashing. It describes developing a conceptual toolkit to understand fundamental file concepts and generic operations. It also discusses how to create an object-oriented toolkit to make file structures usable by implementing them as classes. Finally, it provides an overview of using objects in C++ for file structure design through features like classes, constructors, and operator overloading.
This document provides an overview of Bionimbus and the Open Cloud Consortium (OCC). Bionimbus is an open source cloud for biomedical research that provides services like elastic computing, databases, data transport and analysis pipelines. The OCC operates open clouds and develops standards to bridge private and public clouds. It runs an Open Cloud Testbed and is working to build an Open Science Data Cloud. The OCC aims to develop interoperable cloud architectures and operate infrastructure at data center scale to support open science.
This document summarizes information about the Intelligent Database Systems Lab at KAIST, including its members, activities, and research interests. The lab is led by Professor Hyun Soon Joo and includes 3 PhD students, 3 MS students, and 2 foreign interns. Their research focuses on context-aware computing, social-aware computing, sensor databases for wireless sensor networks, and healthcare applications using sensors. They have annual events like a home-coming day and winter workshop.
Exploration of multidimensional biomedical data in pub chem, Presented by Lia...Lucidworks (Archived)
The document discusses the development of a new search system for PubChem to allow for exploration of multidimensional biomedical data. The new system was needed to address the challenges of handling large and heterogeneous datasets with many relationships between data types in a way that allows for fast querying. The system leverages Apache SOLR to provide features like full text search, faceting, molecule structure searching and joining of related data. It includes backend components like SOLR, SQL and specialized search engines as well as web APIs and frontend interfaces like reusable widgets and a new search interface.
This document outlines a project between the Odum Institute and IQSS Dataverse team to integrate the Dataverse data repository system with iRODS, an open source data management system. The goals are to expand storage options for Dataverse, integrate curation workflows, and connect Dataverse to national research data infrastructure. A prototype will be developed to enable automated ingest of data from Dataverse to iRODS using rules and APIs. Challenges include migrating both systems to newer versions while maintaining authentication between them. An initial prototype is expected in August 2015.
Lessons Learned from a Year's Worth of Benchmarking Large Data Clouds (Robert...Robert Grossman
The document summarizes Sector, an open-source large data cloud computing platform, and compares it to Hadoop. Sector uses a file-based storage system instead of Hadoop's block-based HDFS, and features a more flexible UDF programming model compared to MapReduce. Benchmark results show Sector outperforming Hadoop on the Terasort and MalStone benchmarks, with speedups of up to 19x, due to its dataflow balancing, UDP-based transport, and other architectural advantages over Hadoop for data-intensive computing at scale. Lessons learned include the importance of data locality, load balancing, and fault tolerance in large-scale systems.
Accumulo Summit 2016: Accumulo in the EnterpriseAccumulo Summit
Many organizations are looking to Hadoop clusters in order to store and manage an ever-increasing amount of data. As the volume and variety of data in these systems grows, administrators are being confronted with more information, from more sources, than they have ever seen concentrated in a single place. The responsibility for securing all this data can be daunting to an administrator, even intimidating. Could the answer lie in Accumulo?
Conventional approaches to data security usually do not suffice for this scenario. They are often coarse-grained, applying only at the file or table level. In a world where arbitrary compute tasks can be pushed into the cluster, defining a security perimeter is difficult or impossible. On the other hand, relegating access policy enforcement to the application level instead of the database level ultimately invites a security disaster.
This is the world that Chief Security Officers, Chief Information Officers, and Chief Data Officers live in, and the problem of security for big data is the single biggest impediment to delivering a Hadoop-based solution in the enterprise’s production network. Numerous organizations have implemented Hadoop as a pilot, but find themselves blocked by similar considerations when the time to move into production:
• How do you implement fine-grained access controls in a Hadoop system?
• What about encryption at rest? Encryption in motion?
• How will this tie into our identity infrastructure?
• How will this fit into our operational workflow?
This keynote will explore the ways in which Apache Accumulo is uniquely positioned to mitigate or resolve problems around access control and security for big data, thus enabling Hadoop clusters to move from pilot to production.
– Speaker –
Russ Weeks
Software Architect, PHEMI Systems
Russ Weeks is a Software Architect at PHEMI. Prior to joining PHEMI Systems, Russ worked in the network management groups at Ericsson and Cray Supercomputers, where he discovered a passion for distributed data structures and algorithms. PHEMI Systems is a Vancouver, BC-based startup focused on the storage, retention and governance of structured and unstructured data.
— More Information —
For more information see http://www.accumulosummit.com/
This document provides an overview of Circos, a software package for visualizing data in circular form. It discusses Circos' installation process, file distribution, and generation of Circos plots. Circos allows researchers and data analysts to represent data at different levels in a circular layout and visualize network flows.
This document provides an overview of e-discovery and why it should be a priority for organizations. Key points include:
- E-discovery is the process of identifying, preserving, and producing electronic information for legal cases. It is important due to the large volume of electronic data and growing legal obligations.
- Getting the right e-discovery capabilities can help organizations respond more effectively to lawsuits, potentially avoid lawsuits, and reduce costs of email management and litigation.
- The document outlines important e-discovery lessons from past court cases and why data from multiple sources may need to be included in e-discovery processes.
IntellaCore is a company that helps other businesses become globally competitive without requiring significant capital investments. It does this by evaluating companies, helping them enter new international markets, and introducing them to new global vendors and suppliers. Case studies show how IntellaCore has helped companies expand into countries like Canada, the UK, and others by providing market intelligence and connecting them with potential partners. Business owners praise IntellaCore for providing measurable results that have helped grow their distribution and sales globally.
Delta InfraSuite is Delta Electronics' data center infrastructure solution that includes integrated power, cooling, rack, and management systems. It offers modular components that allow for scalable and efficient data center design. Key features include optimized power distribution and cooling, energy savings, easy installation and operation, and a centralized environment management system. Delta InfraSuite aims to help IT managers address the challenges of building and maintaining high-performance, eco-friendly data centers.
E Discovery General E Discovery Presentationjvanacour
This document provides an overview of key concepts and best practices regarding electronic discovery (e-discovery). It discusses the duty to preserve relevant evidence once litigation is reasonably anticipated. It also outlines the stages of managing e-discovery, including having a reasonable document retention policy prior to notice, issuing a litigation hold once notice is received, and complying with discovery requests once litigation begins. The document emphasizes communicating preservation obligations, overseeing preservation efforts, and producing electronic documents and metadata in a usable format.
This document provides an overview of electronic discovery (eDiscovery) including defining electronically stored information (ESI), understanding the eDiscovery process, and Hudson Legal's role. It explains that ESI includes emails, documents, social media posts and more. The eDiscovery process is broken down into the nine phases of the Electronic Discovery Reference Model (EDRM) including identification, preservation, collection, processing, review, analysis, production and presentation. Hudson Legal assists with project management, document review, and quality control during the review process.
Digital forensics involves analyzing digital artifacts like computers, storage devices, and network traffic as potential legal evidence. The process includes preparing investigators, collecting evidence while maintaining a chain of custody, examining and analyzing the data, and reporting the results. Key steps are imaging systems to obtain an exact duplicate without altering the original, recovering volatile data from memory, and using tools like EnCase and The Sleuth Kit to manually review and search the evidence for relevant information.
Computer forensics is a branch of digital forensic science involving the legal investigation and analysis of evidence found in computers and digital storage media. The objectives are to recover, analyze, and preserve digital evidence in a way that can be presented in a court of law, and to identify evidence and assess the identity and intent of perpetrators in a timely manner. Computer forensics techniques include acquiring, identifying, evaluating, and presenting digital evidence found in files, databases, audio/video files, websites, and other locations on computers, as well as analyzing deleted files, network activity, and detecting steganography.
Computer forensics is the “who, what, when, and how” of electronic evidence. Typically narrow in scope, it attempts to reconstruct events, focusing on the computer-based conduct of an individual or group of individuals. The types of cases involving computer forensics are numerous and varied – from the personal (i.e. locating hidden assets in a messy divorce case), to the political (i.e. investigating alleged misuse of government computers for political gain), to the dramatic (i.e. “What was your client’s former
employee downloading from the Internet before he was fired
and brought suit for wrongful termination?”).
This document provides an overview of computer forensics. It defines computer forensics as using analytical techniques to identify, collect, and examine digital evidence. The objective is usually to provide evidence of specific activities. Computer forensics is used for cases like employee internet abuse, data theft, fraud, and criminal investigations. The document outlines the history, approaches, tools, advantages, and disadvantages of computer forensics. It describes securing systems, recovering files, decrypting data, and documenting procedures used in investigations.
The presentation is all about computer forensics. the process , the tools and its features and some example scenarios.. It will give you a great insight into the computer forensics
Digital forensics is the preservation, identification, extraction and documentation of computer evidence for use in courts. There are various branches including network, firewall, database and mobile device forensics. Digital forensics helps solve cases of theft, fraud, hacking and viruses. Challenges include increased data storage, rapid technology changes and lack of physical evidence. Three case studies showed how digital forensics uncovered evidence through encrypted communications, text messages and diverted drug operations. The future of digital forensics includes more sophisticated tools and techniques to analyze large amounts of data.
This document provides an overview of computer forensics. It defines computer forensics as the process of preserving, identifying, extracting, documenting and interpreting computer data for legal evidence. The document outlines the history of the field from the 1970s to present day, describes the typical steps of acquisition, identification, evaluation and presentation, and discusses certifications, requirements, evidence collection, uses, advantages and disadvantages of computer forensics. It concludes that computer forensics is needed to uncover electronic evidence for prosecuting cybercrimes.
This document provides an overview of computer forensics. It defines computer forensics as identifying, preserving, analyzing and presenting digital evidence in a legally acceptable manner. The objective is to find evidence related to cyber crimes. Computer forensics has a history in investigating financial fraud, such as the Enron case. It describes the types of digital evidence, tools used, and steps involved in computer forensic investigations. Key points are avoiding altering metadata and overwriting unallocated space when collecting evidence.
Computer forensics involves identifying, preserving, analyzing, and presenting digital evidence from computers or other electronic devices in a way that is legally acceptable. The main goal is not only to find criminals, but also to find evidence and present it in a way that leads to legal action. Cyber crimes occur when technology is used to commit or conceal offenses, and digital evidence can include data stored on computers in persistent or volatile forms. Computer forensics experts follow a methodology that involves documenting hardware, making backups, searching for keywords, and documenting findings to help with criminal prosecution, civil litigation, and other applications.
Analytics with unified file and object Sandeep Patil
Presentation takes you through on way to achive in-place hadoop based analytics for your file and object data. Also give you example of storage integration with cloud congnitive services
In Place Analytics For File and Object DataSandeep Patil
The document discusses IBM Spectrum Scale's unified file and object access feature. It introduces Spectrum Scale and its support for file and object access. The unified file and object access feature allows data to be accessed as both files and objects without copying, through a single management plane. Use cases like in-place analytics for object data and common identity management across file and object access are enabled. A demo is presented where a file is uploaded as an object, analytics is run on it, and the result downloaded as an object, without data movement.
The document discusses IBM Spectrum Scale's unified file and object access feature. It allows data to be accessed as both files and objects within the same namespace without data copies. This enables use cases like running analytics directly on object data using Hadoop/Spark without data movement. It also allows publishing analytics results back as objects. The feature supports common user authentication for both file and object access and flexible identity management modes. A demo is shown of uploading a file as object, running analytics on it, and downloading the results as object.
This document discusses the JPL Media Search Project, a multimedia search tool developed by JPL and Owl Insight LLC to index and search audio/video files. It can perform semantic searches to find relevant content without knowing exact search terms. The tool was piloted on a set of 1700 files. Plans are described to scale the system and apply it to larger collections like the NASA Engineering Network repository containing over 1 million files. The goal is to help NASA effectively capture and retrieve engineering best practices and expertise contained in multimedia files.
Get a glimpse of the main features supported in Nuxeo Platform LTS 2015.
With this LTS version of the Nuxeo Platform, we’re changing how we assign product version names and numbers. The name for each LTS version is now based on the release year. Nuxeo Platform LTS 2015 is the result of the four Fast Track releases throughout the past year.
Highlights of Nuxeo Platform LTS 2015 include:
- Nuxeo Live Connect: Native Integration with Google Drive & Dropbox
- Content Analytics & Data Visualisation
- Elasticsearch: API Passthrough, Hints for NXQL, Security
- Massive Scalability with MongoDB Integration
- New Document Viewer
- Automation Scripting
- Nuxeo Drive 2
- Automated Media Conversions
Accesso ai dati con Azure Data PlatformLuca Di Fino
The document discusses various data storage options available on the Microsoft Azure platform. It provides information on relational databases like Azure SQL, non-relational databases like Azure Table Storage and DocumentDB, file storage with Azure Blobs, queue-based messaging with Azure Queues, and data analytics services like HDInsight. Live demos are shown of common tasks like inserting, querying and retrieving data from Table Storage, Blob Storage, and Queues. Key differences between relational and non-relational storage are also explained.
WoSC19: Serverless Workflows for Indexing Large Scientific DataUniversity of Chicago
The use and reuse of scientific data is ultimately dependent on the ability to understand what those data represent, how they were captured, and how they can be used. In many ways, data are only as useful as the metadata available to describe them. Unfortunately, due to growing data volumes, large and distributed collaborations, and a desire to store data for long periods of time, scientific “data lakes” quickly become disorganized and lack the metadata necessary to be useful to researchers. New automated approaches are needed to derive metadata from scientific files and to use these metadata for organization and discovery. Here we describe one such system, Xtract, a service capable of processing vast collections of scientific files and automatically extracting metadata from diverse file types. Xtract relies on function as a service models to enable scalable metadata extraction by orchestrating the execution of many, short-running extractor functions. To reduce data transfer costs, Xtract can be configured to deploy extractors centrally or near to the data (i.e., at the edge). We present a prototype implementation of Xtract and demonstrate that it can derive metadata from a 7 TB scientific data repository.
The document discusses security and privacy considerations for big data systems. It proposes two main areas of focus: 1) developing new security and privacy design patterns tailored for big data, and 2) defining a "big data security fabric" to orchestrate security across big data tools and technologies. The document also provides examples of how security innovations from Apache Storm could be integrated into other frameworks to provide features like authentication, authorization, and data isolation. Finally, it discusses challenges around clarifying the definition of a "security fabric" and leveraging existing models to guide development while addressing the unique aspects of big data technologies and use cases.
PlayBox MAM has over 100 successful installations worldwide now. With the help of our MAM solution, you can digitize your content and further manage and organize media files (Videos, Audios, Documents and Photographs) located on different computers or storage devices on a network thereby creating an online e-library for you. You can analyze your assets, retrieve and edit the metadata, create sub-items and archive them with security.
This document provides an overview of big data and Apache Hadoop. It defines big data as large and complex datasets that are difficult to process using traditional database management tools. It discusses the sources and growth of big data, as well as the challenges of capturing, storing, searching, sharing, transferring, analyzing and visualizing big data. It describes the characteristics and categories of structured, unstructured and semi-structured big data. The document also provides examples of big data sources and uses Hadoop as a solution to the challenges of distributed systems. It gives a high-level overview of Hadoop's core components and characteristics that make it suitable for scalable, reliable and flexible distributed processing of big data.
Дмитрий Попович "How to build a data warehouse?"Fwdays
To build a data warehouse, Tubular ingests raw data from multiple sources using Kafka and stores it permanently. The data is normalized using Spark - duplicates are removed, data is partitioned by time, and sources are joined. A metadata storage using Hive Metastore allows unified access to datasets discovered across various storage formats like Parquet and Avro. This centralized repository helps engineers, analysts and services access and analyze disparate data.
CESSI is an organization in Argentina that produces knowledge-based content but had difficulties sharing it. They implemented kbee.docs, a document management system, to create a digital library. Kbee.docs allows for secure uploading, organizing, searching, and sharing of documents and multimedia content. It provides tools for classification, security policies, and collaboration without requiring technical expertise or ongoing maintenance.
Filebeat Elastic Search Presentation.pptxKnoldus Inc.
In this session, we will figure out how you can use Filebeat to monitor the Elasticsearch log files, collect log events, and ship them to the monitoring cluster. And how your recent logs are visible on the Monitoring page in Kibana.
RUresearch: Supporting the Management and Preservation of Research Data - Ale...ASIS&T
RUresearch: Supporting the Management and Preservation of Research Data
Aletia Morgan
Presentation at Research Data Access & Preservation Summit
22 March 2012
Introduction to Object Storage Solutions White PaperHitachi Vantara
Learn more about Hitachi Content Platform Anywhere by visiting http://www.hds.com/products/file-and-content/hitachi-content-platform-anywhere.html
and more information on the Hitachi Content Platform is at http://www.hds.com/products/file-and-content/content-platform
INtime RTOS is a deterministic, real-time operating system that runs on multi-core PC hosts. It allows critical real-time applications to run alongside Windows applications by explicitly partitioning host resources like cores and memory. INtime RTOS provides object-based services and communication between processes across nodes through its IPC mechanism. Solutions built on INtime RTOS can be deployed either on the same host as Windows or on distributed hosts.
In this session, we’ll focus exclusively on OpenStack Swift, OpenStack’s object store capability. We’ll review the architecture, use cases, deployment strategies and common obstacles as we “open up the covers” on this exciting element of the OpenStack architecture.