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# LOT-912 IBM LotusLive 2010 Train 2 Technical(R) Specialist

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LOT-912 exam Dumps Source : IBM LotusLive 2010 Train 2 Technical(R) Specialist

Test Code : LOT-912
Test Name : IBM LotusLive 2010 Train 2 Technical(R) Specialist
Vendor Name : IBM
Q&A : 80 Real Questions

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# IBM IBM LotusLive 2010 Train

### IBM Launches LotusLive Labs; Opens Up Collaboration Platform's API To partners | killexams.com Real Questions and Pass4sure dumps

At IBM’s annual conference, Lotusphere, massive Blue has announced innovations to its cloud-based mostly collaboration platform, LotusLive. LotusLive gives commercial enterprise users with online email, internet conferencing, social community and collaboration applications inside the cloud.

To spur innovation around the platform, IBM is formally launching LotusLive Labs, an R&D pipeline that mixes the substances of IBM research with Lotus. The challenge is kicking off with a collection of latest LotusLive technologies on the conference together with Slide Library, a collaborative technique to build and share displays; Collaborative Recorded meetings, a carrier that records and immediately transcribes assembly displays and audio/video for searching and tagging; experience Maps, a means to visualize and have interaction with conference schedules; and Composer, the capacity to create LotusLive mashups in the course of the mixture of the platform’s capabilities. mission concord will additionally debut as a web-based doc editor for developing and sharing files, presentations and spreadsheets. And IBM might be including LotusLive assist for the iPhone by means of Labs.

large Blue is additionally opening up LotusLive’s API to 3rd-celebration developers (who need to be an IBM enterprise accomplice). prior to now, the platform’s API turned into simplest accessible through a specific software however now all IBM companions can construct upon the collaboration suite know-how. as an instance, Salesforce.com will offer an integration of its CRM application with LotusLive and Skype will additionally present the capacity to integrate with LotusLive contacts.

IBM will be rolling out a new version of its electronic mail offering inside LotusLive, LotusLive Notes, that allows you to have upgraded connectivity to cell devices, facts migration options, and flexible storage decisions. in addition, the new customer will aid hybrid on-premise and public cloud deployments.

LotusLive received a lift closing week as Panasonic introduced that it became switching over to IBM’s on-line collaboration suite from Microsoft exchange. This was a major win for IBM since the deal represented the biggest commercial enterprise cloud deployment thus far, with over 100,000 Panasonic personnel to make use of LotusLive.

while this coup strengthens IBM’s vicinity within the collaboration suite cloud, Microsoft is additionally aggressively pursuing the cloud, with a contemporary $250 million cloud computing deal with HP. And Microsoft is pushing its collaboration offerings online with workplace 2010. As more and more agencies look to the cloud for collaboration and productivity suites, the landscape to provide these features is fitting extraordinarily competitive. Google is also a powerful competitor in the house with its Google Apps business providing, and VMware simply upped its stake with the acquisition of Zimbra from Yahoo. Startup Zoho, is also growing to be at a speedy tempo. ### Panasonic Drops change, Opts for IBM LotusLive | killexams.com Real Questions and Pass4sure dumps information Panasonic Drops alternate, Opts for IBM LotusLive • 01/14/2010 • Panasonic has chosen IBM to give hosted electronic mail and collaboration services for its global group of workers. The electronics brand is making the flow to better connect its personnel, companions and suppliers international, in accordance with an announcement issued on Thursday via IBM. The deal contains electronic mail, file sharing, web conferencing and collaboration capabilities. Panasonic is planning to gradually migrate from using Microsoft exchange as its basic premises-installed e mail server. as an alternative, Panasonic will use IBM's hosted LotusLive.com functions for e-mail, contacts and calendar aid. additionally, IBM's LotusLive Connections provider will provide Panasonic with a social networking solution. A spokesperson for IBM observed that Panasonic expects to connect 100,000 clients international this yr using the functions. although, in the subsequent two years, that number may additionally expand to more than 300,000 clients. LotusLive services use IBM's federation and encryption technologies for electronic mail safety. IBM at the moment offers six LotusLive services: Connections, engage, movements, meetings, Notes and iNotes. The features will also be ordered a la carte. however, within the case of Panasonic, IBM established a bundled service deal, according to the spokesperson. The choice to go with LotusLive came after Panasonic investigated offerings from Cisco, IBM, Google and Microsoft. Cisco and Google had been eradicated early in the method, the spokesperson said. Late remaining yr, IBM rolled out a calendar and electronic mail service known as LotusLive iNotes, which is designed for transportable gadgets. iNotes is a light-weight, pure cloud-based offering that stems from IBM's acquisition of Hong Kong-based mostly Outblaze Ltd.'s messaging solution in April 2009 IBM offers a 30-day trial of LotusLive, which is purchasable for gratis. IBM now presents LotusLive in eight extra languages. Kurt Mackie is senior information producer for the 1105 enterprise Computing neighborhood. ### The Radicati group Releases "IBM Lotus Notes/Domino Market analysis, 2010-2014" | killexams.com Real Questions and Pass4sure dumps supply: The Radicati group, Inc. June 07, 2010 07:00 ET a new look at From the Radicati group, Inc. provides extensive put in Base Breakouts by means of version, area and business measurement for IBM Lotus Domino, IBM Lotus Notes, and IBM LotusLive PALO ALTO, CA--(Marketwire - June 7, 2010) - The Radicati neighborhood, Inc.'s newest study, "IBM Lotus Notes/Domino Market analysis, 2010-2014," gives an in-depth evaluation of the marketplace for IBM Lotus Domino, IBM Lotus Notes, and IBM LotusLive, together with market share, installed base by means of edition, in addition to breakouts by way of vertical business, enterprise measurement, and vicinity. in response to the record, IBM Lotus Domino could have an put in base of 192 million on-premise and hosted mailboxes by means of 12 months-end 2010, and is expected to grow to a complete of 266 million mailboxes by way of 2014. This represents a regular annual boom rate of eight%. The record specializes in IBM Lotus Domino and IBM Lotus Notes, in addition to IBM's different electronic mail and Collaboration items, similar to IBM LotusLive, IBM Lotus Notes traveler, and IBM Lotus iNotes. The file also covers IBM Lotus' other collaboration items, akin to IBM Lotus Sametime, IBM Lotus Connections, IBM Lotus Symphony, IBM Lotus Quickr, and IBM Lotus Protector. To order a replica of the look at, or for additional information about our market analysis programs, please contact us at (650) 322-8059, or talk over with our web web page at http://www.radicati.com. about the Radicati neighborhood, Inc. The Radicati group is a leading expertise analysis and advisory firm concentrated on all points of email, protection, electronic mail archiving, regulatory compliance, wireless technologies, net services, quick messaging, unified communications, social networking, and greater. 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A report, called "Big data: Harnessing a game-changing asset" (PDF) by the Economist Intelligence Unit and sponsored by SAS, quotes Peter Fader, professor of marketing at the University of Pennsylvania's Wharton School, as saying that the big-data trend is not a boon to businesses right now, as the volume and velocity of the data reduces the time we spend analysing it. "In some ways, we are going in the wrong direction," he said. "Back in the old days, companies like Nielsen would put together these big, syndicated reports. They would look at market share, wallet share and all that good stuff. But there used to be time to digest the information between data dumps. Companies would spend time thinking about the numbers, looking at benchmarks and making thoughtful decisions. But that idea of forecasting and diagnosing is getting lost today, because the data are coming so rapidly. In some ways we are processing the data less thoughtfully." One might argue that there's limited competitive advantage to spending hours mulling over the ramifications of data that everyone's got, and that big data is about using new data and creating insights that no one else has. Even so, it's important to assign meaning and context to data quickly, and in some cases this might be difficult. Henry Sedden, VP of global field marketing for Qlikview, a company that specialises in business intelligence (BI) products, calls the masses of data that organisations are hoping to pull in to their big-data analyses "exhaust data". He said that in his experience, companies aren't even managing to extract information from their enterprise resource-planning systems, and are therefore not ready for more complex data analysis. "I think it's a very popular conversation for vendors to have," he said, "but most companies, they are struggling to deal with the normal data in their business rather than what I call the exhaust data." Deloitte director Greg Szwartz agrees. "Sure, if we could crack the code on big data, we'd all be swimming in game-changing insights. Sounds great. But in my day-to-day work with clients, I know better. They're already waging a war to make sense of the growing pile of data that's right under their noses. Forget big data — those more immediate insights alone could be game changers, and most companies still aren't even there yet. Even worse, all this noise about big data threatens to throw them off the trail at exactly the wrong moment." However, Gartner analyst Mark Beyer believes there can be no such thing as data overload, because big data is a fundamental change in the way that data is seen. If firms don't grapple with the masses of information that big data enables them to, they will miss out on an opportunity that will see them outperform their peers by 20 per cent in 2015. A recent O'Reilly Strata Conference survey of 100 conference attendees found that: • 18 per cent already had a big-data solution • 28 per cent had no plans at the time • 22 per cent planned to have a big-data solution in six months • 17 per cent planned to have a big-data solution in 12 months • 15 per cent planned to have a big-data solution in two years. • A US survey by Techaisle of 800 small to medium businesses (SMBs) showed that despite their size, one third of the companies that responded were interested in introducing big data. A lack of expertise was their main problem. Seeing these numbers, can companies afford not to jump on the bandwagon? Is data being created too fast for us to process?(Pipe stream image by Prophet6, royalty free) Is there a time when it's not appropriate? Szwartz doesn't think that companies should dive in to big data if they don't think it will deliver the answers they're looking for. This is something that Jill Dyché, vice president of Thought Leadership for DataFlux Corporation, agrees with. "Business leaders must be able to provide guidance on the problem they want big data to solve, whether you're trying to speed up existing processes (like fraud detection) or introduce new ones that have heretofore been expensive or impractical (like streaming data from "smart meters" or tracking weather spikes that affect sales). If you can't define the goal of a big-data effort, don't pursue it," she said in a Harvard Business Review post. This process requires understanding as to which data will provide the best decision support. If the data that is best analysed using big-data technologies will provide the best decision support, then it's likely time to go down that path. If the data that is best analysed using regular BI technologies will provide the best decision support, then perhaps it's better to give big data a miss. How is big data different to BI? Fujitsu Australia executive general manager of marketing and chief technology officer Craig Baty said that while BI is descriptive, by looking at what the business has done in a certain period of time, the velocity of big data allows it to be predictive, providing information on what the business will do. Big data can also analyse more types of data than BI, which moves it on from the structured data warehouse, Baty said. Matt Slocum from O'Reilly Radar said that while big data and BI both have the same aim — answering questions — big data is different to BI in three ways: 1. It's about more data than BI, and this is certainly a traditional definition of big data 2. It's about faster data than BI, which means exploration and interactivity, and in some cases delivering results in less time than it takes to load a web page 3. It's about unstructured data, which we only decide how to use after we've collected it, and [we] need algorithms and interactivity in order to find the patterns it contains. According to an Oracle whitepaper titled "Oracle Information Architecture: An Architect's Guide to Big Data" (PDF), we also treat data differently in big data than we do in BI. Big data is unlike conventional business intelligence, where the simple summing of a known value reveals a result, such as order sales becoming year-to-date sales. With big data, the value is discovered through a refining modelling process: make a hypothesis, create statistical, visual or semantic models, validate, then make a new hypothesis. It either takes a person interpreting visualisations or making interactive knowledge-based queries, or by developing "machine-learning" adaptive algorithms that can discover meaning. And, in the end, the algorithm may be short lived. How can we harness big data? The technologies RDBMS Before big data, traditional analysis involved crunching data in a traditional database. This was based on the relational database model, where data and the relationship between the data were stored in tables. The data was processed and stored in rows. Databases have progressed over the years, however, and are now using massively parallel processing (MPP) to break data up into smaller lots and process it on multiple machines simultaneously, enabling faster processing. Instead of storing the data in rows, the databases can also employ columnar architectures, which enable the processing of only the columns that have the data needed to answer the query and enable the storage of unstructured data. MapReduce MapReduce is the combination of two functions to better process data. First, the map function separates data over multiple nodes, which are then processed in parallel. The reduce function then combines the results of the calculations into a set of responses. Google used MapReduce to index the web, and has been granted a patent for its MapReduce framework. However, the MapReduce method has now become commonly used, with the most famous implementation being in an open-source project called Hadoop (see below). Massively parallel processing (MPP) Like MapReduce, MPP processes data by distributing it across a number of nodes, which each process an allocation of data in parallel. The output is then assembled to create a result. However, MPP products are queried with SQL, while MapReduce is natively controlled via Java code. MPP is also generally used on expensive specialised hardware (sometimes referred to as big-data appliances), while MapReduce is deployed on commodity hardware. Complex event processing (CEP) Complex event processing involves processing time-based information in real time from various sources; for example, location data from mobile phones or information from sensors to predict, highlight or define events of interest. For example, information from sensors might lead to predicting equipment failures, even if the information from the sensors seems completely unrelated. Conducting complex event processing on large amounts of data can be enabled using MapReduce, by splitting the data into portions that aren't related to one another. For example, the sensor data for each piece of equipment could be sent to a different node for processing. Hadoop Derived from MapReduce technology, Hadoop is an open-source framework to process large amounts of data over multiple nodes in parallel, running on inexpensive hardware. Data is split into sections and loaded into a file store — for example, the Hadoop Distributed File System (HDFS), which is made up of multiple redundant nodes on cheap storage. A name node keeps track of which data is on which nodes. The data is replicated over more than one node, so that even if a node fails, there's still a copy of the data. The data can then be analysed using MapReduce, which discovers from the name node where the data needed for calculations resides. Processing is then done at the node in parallel. The results are aggregated to determine the answer to the query and then loaded onto a node, which can be further analysed using other tools. Alternatively, the data can be loaded into traditional data warehouses for use with transactional processing. Apache is considered to be the most noteworthy Hadoop distribution. NoSQL NoSQL database-management systems are unlike relational database-management systems, in that they do not use SQL as their query language. The idea behind these systems is that that they are better for handling data that doesn't fit easily into tables. They dispense with the overhead of indexing, schema and ACID transactional properties to create large, replicated data stores for running analytics on inexpensive hardware, which is useful for dealing with unstructured data. Cassandra Cassandra is a NoSQL database alternative to Hadoop's HDFS. Hive Databases like Hadoop's file store make ad hoc query and analysis difficult, as the programming map/reduce functions that are required can be difficult. Realising this when working with Hadoop, Facebook created Hive, which converts SQL queries to map/reduce jobs to be executed using Hadoop. Vendors There is scarcely a vendor that doesn't have a big-data plan in train, with many companies combining their proprietary database products with the open-source Hadoop technology as their strategy to tackle velocity, variety and volume. For an idea of how many vendors are operating in each area of the big-data realm, this big-data graphic from Forbes is useful. Many of the early big-data technologies came out of open source, posing a threat to traditional IT vendors that have packaged their software and kept their intellectual property close to their chests. However, the open-source nature of the trend has also provided an opportunity for traditional IT vendors, because enterprise and government often find open-source tools off-putting. Therefore, traditional vendors have welcomed Hadoop with open arms, packaging it in to their own proprietary systems so they can sell the result to enterprise as more comfortable and familiar packaged solutions. Below, we've laid out the plans of some of the larger vendors. Cloudera Cloudera was founded in 2008 by employees who worked on Hadoop at Yahoo and Facebook. It contributes to the Hadoop open-source project, offering its own distribution of the software for free. It also sells a subscription-based, Hadoop-based distribution for the enterprise, which includes production support and tools to make it easier to run Hadoop. Since its creation, various vendors have chosen Hadoop distribution for their own big-data products. In 2010, Teradata was one of the first to jump on the Cloudera bandwagon, with the two companies agreeing to connect the Hadoop distribution to Teradata's data warehouse so that customers could move information between the two. Around the same time, EMC made a similar arrangement for its Greenplum data warehouse. SGI and Dell signed agreements with Cloudera from the hardware side in 2011, while Oracle and IBM joined the party in 2012. Hortonworks Cloudera rival Hortonworks was birthed by key architects from the Yahoo Hadoop software engineering team. In June 2012, the company launched a high-availability version of Apache Hadoop, the Hortonworks Data Platform on which it collaborated with VMware, as the goal was to target companies deploying Hadoop on VMware's vSphere. Teradata has also partnered with Hortonworks to create products that "help customers solve business problems in new and better ways". Teradata Teradata made its move out of the "old-world" data-warehouse space by buying Aster Data Systems and Aprimo in 2011. Teradata wanted Aster's ability to manage "a variety of diverse data that is not structured", such as web applications, sensor networks, social networks, genomics, video and photographs. Teradata has now gone to market with the Aster Data nCluster, a database using MPP and MapReduce. Visualisation and analysis is enabled through the Aster Data visual-development environment and suite of analytic modules. The Hadoop connecter, enabled by its agreement with Cloudera, allows for a transfer of information between nCluster and Hadoop. Oracle's big-data appliance(Credit: Oracle) Oracle Oracle made its big-data appliance available earlier this year — a full rack of 18 Oracle Sun servers with 864GB of main memory; 216 CPU cores; 648TB of raw disk storage; 40Gbps InfiniBand connectivity between nodes and engineered systems; and 10Gbps Ethernet connectivity. The system includes Cloudera's Apache Hadoop distribution and manager software, as well as an Oracle NoSQL database and a distribution of R (an open-source statistical computing and graphics environment). It integrates with Oracle's 11g database, with the idea being that customers can use Hadoop MapReduce to create optimised datasets to load and analyse in the database. The appliance costs US$450,000, which puts it at the high end of big-data deployments, and not at the test and development end, according to analysts.

IBM

IBM combined Hadoop and its own patents to create IBM InfoSphere BigInsights and IBM InfoSphere Streams as the core technologies for its big-data push.

The BigInsights product, which enables the analysis of large-scale structured and unstructured data, "enhances" Hadoop to "withstand the demands of your enterprise", according to IBM. It adds administrative, workflow, provisioning and security features into the open-source distribution. Meanwhile, streams analysis has a more complex event-processing focus, allowing the continuous analysis of streaming data so that companies can respond to events.

IBM has partnered with Cloudera to integrate its Hadoop distribution and Cloudera manger with IBM BigInsights. Like Oracle's big-data product, IBM's BigInsights links to: IBM DB2, its Netezza data-warehouse appliance (its high-performance, massively parallel advanced analytic platform that can crunch petascale data volumes); its InfoSphere Warehouse; and its Smart Analytics System.

SAP

At the core of SAP's big-data strategy sits a high-performance analytic appliance (HANA) data-warehouse appliance, unleashed in 2011. It exploits in-memory computing, processing large amounts of data in the main memory of a server to provide real-time results for analysis and transactions (Oracle's rival product, called Exalytics, hit the market earlier this year). Business applications, like SAP's Business Objects, can sit on the HANA platform to receive a real-time boost.

SAP has integrated HANA with Hadoop, enabling customers to move data between Hive and Hadoop's Distributed File System and SAP HANA or SAP Sybase IQ server. It has also set up a "big-data" partner council, which will work to provide products that make use of HANA and Hadoop. One of the key partners is Cloudera. SAP wants it to be easy to connect to data, whether it's in SAP software or software from another vendor.

Microsoft

Microsoft is integrating Hadoop into its current products. It has been working with Hortonworks to make Hadoop available on its cloud platform Azure, and on Windows Servers. The former is available in developer preview. It already has connectors between Hadoop, SQL Server and SQL Server Parallel Data Warehouse, as well as the ability for customers to move data from Hive into Excel and Microsoft BI tools, such as PowerPivot.

EMC

EMC has centred its big-data technology on technology that it acquired when it bought Greenplum in 2010. It offers a unified analytics platform that deals with web, social, document, mobile machine and multimedia data using Hadoop's MapReduce and HDFS, while ERP, CRM and POS data is put into SQL stores. The data mining, neural nets and statistics analysis is carried out using data from both sets, which is fed in to dashboards.

What are firms doing with these products?

Now that there are products that make use of big data, what are companies' plans in the space? We've outlined some of them below.

Ford

Ford is experimenting with Hadoop to see whether it can gain value out of the data it generates from its business operations, vehicle research and even its customers' cars.

"There are many, many sensors in each vehicle; until now, most of that information was [just] in the vehicle, but we think there's an opportunity to grab that data and understand better how the car operates and how consumers use the vehicles, and feed that information back into our design process and help optimise the user's experience in the future, as well," Ford's big-data analytics leader John Ginder said.

HCF

HCF has adopted IBM's big-data analytics solution, including the Netezza big-data appliance, to better analyse claims as they are made in real time. This helps to more easily detect fraud and provide ailing members with information they might need to stay fit and healthy.

Klout

Klout's job is to create insights from the vast amounts of data coming in from the 100 million social-network users indexed by the company, and to provide those insights to customers. For example, Klout might provide information on how certain peoples' influence on social networks (or Klout score) might affect word-of-mouth advertising, or provide information on changes in demand. To deliver the analysis on a shoestring, Klout built custom infrastructure on Apache Hadoop, with a separate data silo for each social network. It used custom web services to extract data from the silos. However, maintaining this customised service was very complicated and took too long, so the company implemented a BI product based on Microsoft SQL Server 2012 and the Hive data-warehouse system, in which it consolidated the data from the silos. It is now able to analyse 35 billion rows of data each day, with an average response time of 10 seconds for a query.

Mitsui knowledge industry

Mitsui analyses genomes for cancer research. Using HANA, R and Hadoop to pre-process DNA sequences, the company was able to shorten genome-analysis time from several days to 20 minutes.

Nokia

Nokia has many uses for the information generated by its phones around the world; for example, using that information to build maps that predict traffic density or create layered elevation models. Developers had been putting the information from each mobile application into data silos, but the company wanted to have all of the data that's collected globally to be combined and cross referenced. It therefore needed an infrastructure that could support terabyte-scale streams of unstructured data from phones, services, log files and other sources, and computational tools to carry out analyses of that data. Deciding that it would be too expensive to pull the unstructured data into a structured environment, the company experimented with Apache Hadoop and Cloudera's CDH (PDF). Because Nokia didn't have much Hadoop expertise, it looked to Cloudera for help. In 2011, Nokia's central CDH cluster went into production to serve as the company's enterprise-wide information core. Nokia now uses the system to pull together information to create 3D maps that show traffic, inclusive of speed categories, elevation, current events and video.

Walmart

Walmart uses a product it bought, called Muppet, as well as Hadoop to analyse social-media data from Twitter, Facebook, Foursquare and other sources. Among other things, this allows Walmart to analyse in real time which stores will have the biggest crowds, based on Foursquare check-ins.

What are the pitfalls? Do you know where your data is?

It's no use setting up a big-data product for analysis only to realise that critical data is spread across the organisation in inaccessible and possibly unknown locations.

As mentioned earlier, Qlikview's VP of global field marketing, Henry Sedden, said that most companies aren't on top of the data inside their organisations, and would get lost if they tried to analyse extra data to get value from the big-data ideal.

A lack of direction

According to IDC, the big-data market is expected to grow from US$3.2 billion in 2010 to US$16.9 billion in 2015; a compound annual growth rate (CAGR) of 40 per cent, which is about seven times the growth of the overall ICT market.

Unfortunately, Gartner said that through to 2015, more than 85 per cent of the Fortune 500 organisations will fail to exploit big data to gain a competitive advantage.

"Collecting and analysing the data is not enough; it must be presented in a timely fashion, so that decisions are made as a direct consequence that have a material impact on the productivity, profitability or efficiency of the organisation. Most organisations are ill prepared to address both the technical and management challenges posed by big data; as a direct result, few will be able to effectively exploit this trend for competitive advantage."

Unless firms know what questions they want to answer and what business objectives they hope to achieve, big-data projects just won't bear fruit, according to commentariats.

Ovum advised in its report "2012 Trends to Watch: Big Data" that firms should not analyse data just because it's there, but should build a business case for doing so.

"Look to existing business issues, such as maximising customer retention or improving operational efficiency, and determine whether expanding and deepening the scope of the analytics will deliver tangible business value," Ovum said.

Big-data skills are scarce.(IT knowledge image by yirsh, royalty free) Skills shortages

Even if a company decides to go down the big-data path, it may be difficult to hire the right people.

According to Australian research firm Longhaus:

The data scientist requires a unique blend of skills, including a strong statistical and mathematical background, a good command of statistical tools such as SAS, SPSS or the open-source R and an ability to detect patterns in data (like a data-mining specialist), all backed by the domain knowledge and communications skills to understand what to look for and how to deliver it.

This is already proving to be a rare combination; according to McKinsey, the United States faces a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts to analyse big data and make decisions based on their findings.

It's important for staff members to know what they're doing, according to Stuart Long, chief technology officer of Systems at Oracle Asia Pacific.

"[Big data] creates a relationship, and then it's up to you to determine whether that relationship is statistically valid or not," he said.

"The amount of permutations and possibilities you can start to do means that a lot of people can start to spin their wheels. Understanding what you're looking for is the key."

Data scientist DJ Patil, who until last year was LinkedIn's head of data products, said in his paper "Building data science teams" that he looks for people who have technical expertise in a scientific discipline; the curiosity to work on a problem until they have a hypothesis that can be tested; a storytelling ability to use data to tell a story; and enough cleverness to be able to look at a problem in different ways.

He said that companies will either need to hire people who have histories of playing with data to create something new, or hire people who are straight out of university, and put them in to an intern program. He also believes in using competitions to attract data scientist hires.

Privacy

Tracking individuals' data in order to be able to sell to them better will be attractive to a company, but not necessarily to the consumer who is being sold the products. Not everyone wants to have an analysis carried out on their lives, and depending on how privacy regulations develop, which is likely to vary from country to country, companies will need to be careful with how invasive they are with big-data efforts, including how they collect data. Regulations could lead to fines for invasive policies, but perhaps the greater risk is loss of trust.

One illustration of distrust arising from companies using data from people's lives is the famous example from Target, where the company sent coupons to a teenager for pregnancy-related products. Based on her purchasing behaviour, Target's algorithms believed her to be pregnant. Unfortunately, the teenager's father had no idea about the pregnancy, and he verbally abused the company. However, he was forced to admit later that his daughter actually was pregnant. Target later said that it understands people might feel like their privacy is being invaded by Target using buying data to figure out that a customer is pregnant. The company was forced to change its coupon strategy as a result.

Security

Individuals trust companies to keep their data safe. However, because big data is such a new area, products haven't been built with security in mind, despite the fact that the large volumes of data stored mean that there is more at stake than ever before if data goes missing.

There have been a number of highly publicised data breaches in the last year or two, including the breach of hundreds of thousands of Nvidia customer accounts , millions of Sony customer accounts and hundreds of thousands of Telstra customer accounts . The Australian Government has been promising to consider data breach-notification laws since it conducted a privacy review in 2008, but, according to the Office of the Australian Information Commissioner (OAIC), the wait is almost over . The OAIC advised companies to become prepared for a world where they have to notify customers when data is lost. It also said that it would be taking a hard line on companies that are reckless with data.

Steps to big data

Before you go down the path of big data, it's important to be prepared and approach an implementation in an organised manner, following these steps.

• What do you wish you knew? This is where you decide what you want to find out from big data that you can't get from your current systems. If the answer is nothing, then perhaps big data isn't the right thing for you

• What are your data assets? Can you cross reference this data to produce insights? Is it possible to build new data products on top of your assets? If not, what do you need to implement to be able to do so?

• Once you know this, it's time to prioritise. Select the potentially most valuable opportunity for using big-data techniques and technology, and prepare a business case for a proof of concept, keeping in mind the skill sets you'll need to do it. You will need to talk to the owners of the data assets to get the full picture

• Start the proof of concept, and make sure that there's a clear end point, so that you can evaluate what the proof of concept has achieved. This might be the time to give the owner of the data assets to take responsibility for the project

• Once your proof of concept has been completed, evaluate whether it worked. Are you getting real insights delivered? Is the work that went in to the concept bearing fruit? Could it be extended to other parts of the organisation? Is there other data that could be included? This will help you to discover whether to expand your implementation or revamp it.

• So what are you waiting for? It's time to think big.

### Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models | killexams.com real questions and Pass4sure dumps

Calculating flux states using parsimonious FBA

We calculate parsimonious FBA27 solutions for iML1515, a GEM of E. coli K-12 MG165526. Linear programming problems were constructed using the R45 packages sybil46 and sybilccFBA47, and problems were solved using IBM CPLEX version 12.7. A single iteration of this sampling algorithm proceeds as follows: Oxygen uptake was allowed with probability 1/2, and the environment always contained at least one randomly chosen source of each carbon, nitrogen, sulfur, and phosphate. A number of additional sources per element were drawn from a binomial of size 2 with success probability 1/2. Carbon uptake rates were normalized to the number of carbon atoms in the selected substrates. This process was repeated until a growth sustaining environment was found and the flux distribution recorded, concluding the iteration. Using this algorithm, we simulated 10,000 environments, and averaged these flux distributions across environments to arrive at the flux feature.

Calculating MFA-constrained flux states

As an alternative to the flux sampling using parsimonious FBA, experimental data on metabolic flux obtained from metabolic flux analysis (MFA) was utilized (presented in Supplementary Figure 5). Reaction fluxes estimated from MFA were obtained for eight growth conditions for E. coli48. FBA using the E. coli metabolic network reconstruction iML151526 was then used to identify a steady-state flux distribution (vFBA) as close to the MFA-estimated values (vdata) as possible using a quadratic programming (QP) problem:

$${\mathrm{Min}}\mathop {\sum }\limits_i \left( {v_{{\mathrm{FBA}},i} - v_{{\mathrm{data}},i}} \right)^2\: {\rm s.t.}$$

(1)

$${\mathbf{Sv}}_{{\mathrm{FBA}}} = 0$$ $$v_{{\mathrm{lb}},i} < v_{{\mathrm{FBA}},i} < v_{{\mathrm{ub}},i}$$

For each condition, the Pearson correlation between MFA-estimated and FBA-calculated fluxes was greater than 0.99, indicating general concordance between the model used to estimate the MFA fluxes and iML1515.

Measured fluxes were then constrained to their QP-optimized values, and FBA was once again run with an ATP maximization objective (termed the ATP maintenance reaction or ATPM)49 by solving a linear programming (LP) problem:

$${\mathrm{Max}}\,v_{\mathrm{ATPM}}\:\rm s.t.$$

(2)

$${\mathbf{Sv}}_{{\mathrm{FBA}}} = 0$$$$v_{{\mathrm{lb}},i}^ \ast < v_{{\mathrm{FBA}},i}^ \ast < v_{{\mathrm{ub}},i}^ \ast$$

where vlb* and vub* are the standard flux bounds augmented with the QP-optimized values from Eq. (1).

Finally, the objective ATP production reaction was set to its calculated optimal value, and the total flux was minimized subject to all previous constraints as a parsimony objective based on the idea that the cell generally will not carry large amounts of unnecessary flux due to the cost of sustaining the required enzyme levels50.

$${\mathrm{Min}}\,\left\| {{\boldsymbol{v}}_{\rm FBA}} \right\|_2 \, {\rm {s.t.}}$$

(3)

$${\mathbf{Sv}}_{{\mathrm{FBA}}} = 0$$$$v_{{\mathrm{lb}},i}^\# < v_{{\mathrm{FBA}},i}^\# < v_{{\mathrm{ub}},i}^\#$$

where vlb# and vub# are the same flux constraints used in the problem defined in Eq. (2) but now augmented with a constraint on the optimal value of vATPM identified in Eq. (2).

The final flux solutions show good agreement with MFA-estimated flux states, including measured growth rates, while maximizing ATP production and maintaining parsimony as secondary objectives. The average of the final flux solutions in the eight growth conditions was used as the flux feature for the sensitivity analysis shown in Supplementary Figure 5. Problems were set up using the COBRA toolbox version 2.0 in Matlab 2016b and solved using Gurobi 8.0.1 solvers.

Generalist property

Based on the GPR relations provided by iML1515, we use the maximum number of times the gene products catalyzing a given reaction are utilized in other reactions to quantify the generalist feature. The number of substrates for a given reaction were extracted from the stoichiometric matrix of iML1515, excluding water and protons.

Protein sequence and structure property calculations

To gather protein-specific features, global properties of catalytic enzymes and local properties of their active sites were calculated using the ssbio Python package51. First, model reactions in iML1515 were mapped to their protein sequences and 3D structures based on the stored GPR rules. This was done utilizing the UniProt mapping service, allowing gene locus IDs (e.g., b0008) to be mapped to their corresponding UniProt protein sequence entries (e.g., P0A870) and annotated sequence features52. Next, UniProt identifiers were mapped to structures in both the Protein Data Bank29 and homology models from the I-TASSER modelling pipeline31. These structures were then scored and ranked53 to select a single representative structure based on resolution and sequence coverage parameters. For the cases in which only PDB structures were available, the PDBe best structures API was queried for the top scoring structure. If no more than 10% of the termini were missing along with no insertions and only point mutations within the core of the sequence, the structure was set as representative. Otherwise, a homology model was selected by sequence identity percentage or queued for modelling53. It is important to note that the structure selection protocol results in a final structure that is monomeric, and thus parameters which may be impacted by quaternary complex formation are not currently considered. This is a limitation in both experimental data and modelling methods, as complex structures remain a difficult prediction to make. Furthermore, for global and local calculations (described below), all non-protein molecules (i.e., water molecules, prosthetic groups) were stripped before calculating the described feature. Out of the 1515 proteins, 729 experimental protein structures and 784 homology models were used in property calculations. Finally, we added annotated active site locations from the Catalytic Site Atlas SQL database32 for any matching PDB ID in the analysis.

Global protein properties were classified as properties that were derived from the entire protein sequence or structure (e.g., percent disordered residues), and local properties were those that described an annotated catalytic site (e.g., average active site depth from the surface). From the protein sequence, global properties were calculated using the EMBOSS pepstats package54 and the Biopython ProtParam module55. Local properties for secondary structure and solvent accessibilities were predicted from sequence using the SCRATCH suite of tools56 and additionally calculated from set representative structures using DSSP57 and MSMS58. Predicted hydrophobicities of amino acids were calculated using the Kyte-Doolittle scale for hydrophobicity with a sliding window of seven amino acids59. For a full list of obtained properties, see Supplementary Table 2.

Biochemical features

Reaction EC numbers were obtained from the Bigg database60, and extended with additional EC number data from KEGG61 and MetanetX62 where available.

To estimate reaction Gibbs energies, metabolite data for eight growth conditions for E. coli was obtained from literature48. Reaction equilibrium constants (Keqs) were estimated using the latest group contribution method63. Then, a thermodynamic FBA problem64 was solved constraining only high flux reactions (>0.1 mmol/gDW/h), subject to uncertainty. Once a feasible set of fluxes, metabolite concentrations (x), and Keqs was identified, convex sampling was used to obtain a distribution of x and Keq values that accounts for measurement gaps and uncertainty. These sampled x and Keq values were used to calculate the reaction Gibbs energies using the definition:

$$\Delta G = - {\rm RT}{\mathrm{log}}\left( {K_{\mathrm{eq}}} \right) + {\mathrm{log}}\left( Q \right)\\ Q = \mathop {\prod }\limits_i x_i^{S_i}$$

where Q is the reaction quotient defined as the product of the metabolite concentrations (or activities) to the power of their stoichiometric coefficient in the reaction (S). The thermodynamic efficiency parameter ηrev used in this study was then calculated from this ΔG using its definition65:

$$\eta _{\mathrm{rev}} = 1 - {\mathrm{exp}}\left( {\Delta G/{\rm RT}} \right) = 1 - Q/K_{\mathrm{eq}}$$

Note that this expression is bounded between 0 and 1 for reactions in the forward direction (ηrev is 0 at equilibrium and 1 at perfect forward efficiency). For consistency, we considered each reaction as the forward direction stoichiometry for this calculation. Average ηrev across the eight growth conditions was used as model input feature.

Michaelis constants (Kms) were extracted from the BRENDA33 and the Uniprot52 resource and manually curated. When multiple values exist for the same constant, in vivo-like conditions, recency of the study, and agreement among values were used as criteria to select the best value.

The average metabolite concentrations across the eight growth conditions mentioned above48 were used as features on substrate and product concentrations.

Summarizing data across genes

We summarized all features and outputs to the reaction level as given in the metabolic representation of the E. coli metabolic network iML1515. In the case of structural features, which were obtained at the gene-level, we used the GPR relations provided by the model to summarize features. Details are listed in Supplementary Table 1.

Linearization

Features and outputs were transformed to favour linear relationships between features and outputs. Flux, enzyme molecular weight, Km, metabolite concentrations, kcat in vitro, and kapp,max were log-transformed. The reciprocal of temperature was used as suggested by the Arrhenius relationship.

Imputation

The set of features does not contain data on all features for all reactions in iML1515 (See Supplementary Figure 2). To allow GEM predictions, we utilize different imputation strategies: imputation of labelled data, i.e., data that has outputs associated, only, imputation of the unlabelled data only, imputation of both labelled and unlabelled data, and no imputation. Missing observations were imputed using predictive mean matching for continuous data, logistic regression for binary data, and polytomous regression for categorical data of more than two categories (see Supplementary Table 1 for details). This procedure was implemented using the mice package in the R environment45,66. Output data was not used for imputation to prevent optimistic bias in error estimates.

Data on k cat in vitro

We extracted in vitro kcat values for enzymes occurring in the E.coli K-12 MG1655 iML1515 model from the BRENDA33, Sabio34, and Metacyc35 databases. A total of 6812 kcat values were downloaded based on EC numbers. We removed redundant data points that originated from the same experiment in the same publication across databases. When deleting redundant data, we gave preference to the BRENDA and the Metacyc database, in that order. Next, we removed all data explicitly referring to mutated enzymes.

A central problem in using data from these three databases is that many kcat values were measured in the presence of unnatural substrates that are unlikely to occur in physiological conditions. We use the iML1515 model as a resource for naturally occurring metabolic reactions. To use this list as a filter, we mapped reactions from our curated datasets to model reactions. This reaction mapping was implemented using the synonym lists of substrates provided by the MetRxn resource67. Six hundred and sixty four database entries did not contain complete reaction formulas, and we mapped those based on EC numbers and substrate information. We manually checked all entries in the Metacyc dataset with the keyword ‘inhibitor’ in the experimental notes, and omitted data that was measured in the presence of inhibitors. Finally, in cases where multiple literature sources were available, we manually selected sources giving preference to in vivo-like conditions, recency of the study, and agreement among values, making additional use of data in the Uniprot Resource52. In the end, we are left with 497 useable kcat in vitro values that cover 412 metabolic reactions.

Cross validation and hyperparameter tuning

Statistical models of turnover rates were trained using the caret package68 and, in the case of neural networks, the h2o package69. Model hyperparameters were optimized by choosing the set that minimizes cross-validated RMSE in five times repeated (One repetition in the case of neural networks) 5-fold cross-validation. In the case of neural networks, hyperparameters were optimized using 3000 iterations of random discrete search and 5-fold cross-validation. Details on implementation and hyperparameter ranges are given in Supplementary Table 2.

Mechanistic model prediction of protein abundances

In order to validate the ability of different vectors of catalytic turnover rates to explain quantitative protein data, proteome allocation was predicted using the MOMENT algorithm. We calculate MOMENT solutions for iML1515 using turnover rates obtained from the respective data source or ML model. In the case of membrane proteins, which were not in the scope of the ML model, a default value of 65 s−1 was used. Linear programming problems were constructed using the R45 packages sybil46 and sybilccFBA47, and problems were solved using IBM CPLEX version 12.7. Enzyme molecular weights were calculated based on the E. coli K-12 MG1655 protein sequences (NCBI Reference Sequence NC_000913.3), and the total weight of the metabolic proteome was set to 0.32 gprotein/gDW in accordance with the E. coli metabolic protein fraction across diverse growth conditions5,44. Aerobic growth on each substrate in Schmidt et al.37 was modeled by setting the lower bound corresponding to the uptake of the substrate and oxygen to −1000 mmol gDW−1 h−1, effectively leaving uptake rates unconstrained.

In addition to MOMENT, a GEM of metabolism and gene expression (ME model)8,9 was applied to validate the predicted enzyme turnover rates. For these simulations the iJL1678b ME-model of E. coli K-12 MG1655 was used70. Like in the MOMENT predictions, a default value of 65 s−1 was used for the keffs of membrane proteins, and aerobic growth on each substrate in Schmidt et al.37 was modeled by setting the lower bound corresponding to the uptake of the substrate and oxygen to −1000 mmol gDW−1 h−1, effectively leaving uptake unconstrained. The keffs of all processes in iJL1678b-ME that fell outside the scope of iML1515 were also set to 65 s−1. The model was optimized using a bisection algorithm and the qMINOS solver, a solver capable of performing linear optimization in quad-precision71,72, to find the maximum feasible growth rate within a tolerance of 10–14. The unmodeled protein fraction, a parameter to account for expressed proteins that are either outside the scope of the model or underutilized in the model, was set to 0. Further, mRNA degradation processes were excluded from the ME-model for these simulations to prevent high ATP loads at low growth rates.

Genes that are subunits in membrane localized enzyme complexes and genes involved in protein expression processes were out of the scope of the kapp,max and kcat in vitro prediction approaches. Thus these genes were not considered when comparing predicted and measured protein abundances (Fig. 4). In silico predictions that had an abundance greater than zero were matched to experimental protein abundances if the latter contained more than 0 copies/cell. Weight fractions of the metabolic proteome were estimated by normalizing by the sum of masses for in silico predictions and experimental data, respectively.

Statistics

The statistical significance of Spearman’s ρ correlations was tested using the AS 89 algorithm73 as implemented in the cor.test() function of the R environment45. Permutation tests for feature importance in the random forest models were conducted using the R package rfPermute using 500 permutations of the respective response variable per model.

Code availability

R code for model training and analysis, and Python code for ME modelling are available from the authors upon request.

### Internet2 Announces 2016 Technology Exchange Gender Diversity Award Recipients | killexams.com real questions and Pass4sure dumps

MIAMI, Fla., Sept. 26 — Today, Internet2 announced the recipients of four 2016 Technology Exchange gender diversity scholarships. The scholarships recognize talented individuals seeking opportunities to gain hands-on technical experience by attending the event, and spotlights women in the field of IT and their efforts to use technology to serve the faculty, staff and students of their individual institutions.

“Internet2 is proud of our continuing efforts to promote diversity and specifically to support women in the IT and technology fields in higher education,” said Ana Hunsinger, Internet2, vice president of community engagement. “It is a pleasure working so closely with the Internet2 community to ensure inclusivity and opportunities for these women across our member campuses. I’d like to personally congratulate this year’s award recipients and give a special thank you to Pat Burns, Vice President for Information Technology, Colorado State University, Jean Davis, CEO and President, MCNC, John Kolb, CIO, Rensselaer Polytechnic Institute and Marilyn McMillan, CIO, New York University, for helping to support the recognition of these individuals.”

Gender Diversity Award recipients are:

Colleen Morrissey is a Senior Network Engineer at Rensselaer Polytechnic Institute in Troy, New York, and is lead on all network design and implementation projects as well as a member of the security team. For 14 years she also held an Adjunct Lecturer position in the Computer Science department, teaching undergraduate computer network and security classes. Prior to her time at Rensselaer, Colleen worked at a Tier 1 Global ISP in network engineering and operations. Colleen holds a B.S. in Computer Science from Rensselaer Polytechnic Institute.

Tiny Norris, Network Operations Center Coordinator at MCNC, also known as North Carolina Research and Educational Network.  Tiny has worked at MCNC since 2010, first as Network Administrator then as a NOC Coordinator.  Her duties include monitoring and troubleshooting all local and remote network components and its capabilities to ensure operational integrity and timely restoration of services.  She works closely with Network Management Engineers, Knowledge & Information System Engineers and also NOC Engineers in network optimization to provide tireless support to MCNC customers and partners.

MCNC provides technology tools and services to guarantee equal access to the 21st century.  Tiny expects the Technology Exchange will afford her the opportunity to engage with others within the R&E community and to learn, analyze and apply new and better processes to continue to provide a future-proof technology network that is the foundation of change and innovation in educational systems.

Joanna Zwack has worked for Colorado State University’s Academic Computing and Networking Services department since 2015. While her position continues to develop and change, currently she serves as a communication specialist for the Unix team, informing university staff and faculty of developments, training opportunities, and changes. She is also the manager of the university data center. Joanna’s background in elementary education helps her find new ways to communicate with and train members of the university community. She has worked in the IT field for over six years and continues to expand her knowledge of the subject. By being able to attend the Technology Exchange this year, she’s hoping to bring back information and tips that will benefit her entire department.

Gender Diversity Award in recognition of Carrie Regenstein recipient:

Natalie Hidalgo is the Assistant Director of Service Delivery at New York University’s Information Technology organization. In this role she is responsible for developing, implementing, and managing a comprehensive service delivery function across NYU’s IT organization. This role includes the development of IT Service Management processes and the introduction of a technical account management structure. She provides representation and advocacy for clients of NYU’s IT organization at three degree-granting campuses in New York, Abu Dhabi, and Shanghai, and at study-away sites in Africa, Asia, Australia, Europe, North and South America. Coordinating with other NYU administrators, she ensures the delivery of IT services to NYU students, faculty and staff around the world. Prior to her current focus on service delivery, Natalie has worked with the university’s IT division to launch global academic centers, implement an enhanced service model to support faculty in their use of technology, led university-wide workshops on customer service best practices, and introduced new service offerings to the NYU community.

The Internet2 Technology Exchange convenes U.S. and global technology leaders and visionaries including pioneers, technologists, architects, scientists, operators, and students in the fields of networking, security, trust and identity, virtualization, high-performance computing, cloud services, and data storage to share expertise in a forum designed to facilitate the cross-pollination of technical ideas and information.

Featured diversity sessions at this year’s Technology Exchange include:

Diversity and Inclusion in the Internet2 Community

A moderated panel will discuss and address key barriers to the gender diversity challenge and provide an open discussion around topics such as pipeline building, changing the internal IT culture at the campus and system level, changing the macro culture and getting more women involved in high stakes/high impact projects, and acknowledging and addressing challenges in hiring practices.

Gender and Diversity in Information Security and IT

This session will include a panel discussion on gender and diversity in higher education information security and IT, how to improve current diversity levels, and will explore what steps audience members can take to further diversity initiatives.

Internet2 is a member-owned advanced technology community founded by the nation’s leading higher education institutions in 1996. Internet2 provides a collaborative environment for U.S. research and education organizations to solve shared technology challenges, and to develop innovative solutions in support of their educational, research and community service missions. Internet2 also operates the nation’s largest and fastest, coast-to-coast research and education network, with Internet2 Network Operations Center powered by Indiana University. Internet2 serves more than 90,000 community anchor institutions, 317 U.S. universities, 70 government agencies, 42 regional and state education networks, 80 leading corporations working with our community and more than 65 national research and education networking partners representing more than 100 countries.

Source: Internet2

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