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I simply got lower back from attending IBM deem in San Francisco. although it became a brief commute across the nation, i used to be inundated with IBM’s imaginative and prescient, covering topics from A (i.e. synthetic intelligence) to Z (i.e. gear Z) and every runt thing in between.
regardless of the wide-ranging discussion, IBM’s main focal point changed into on three areas: 1) hybrid cloud, 2) advanced analytics, and 3) safety. for instance, IBM’s hybrid cloud discussion based on digital transformation and leaned closely on its purple Hat acquisition, while superior analytics covered simulated intelligence (AI), cognitive computing (Watson), neural networks, and so forth. To panoply its capabilities in these areas, IBM paraded out valued clientele corresponding to Geico, Hyundai credit service provider, and Santander bank, who're making a stake on IBM for video game-altering digital transformation projects.IBM's cybersecurity plans
As for cybersecurity, listed below are a number of of my take-aways about IBM's plans:
IBM’s protection portfolio is pretty solid, and the enterprise appears to be more energized than in the past. After attending IBM think, I upshot hold a couple of cybersecurity suggestions for folks in Armonk and Cambridge, Massachusetts:
In accepted, Armonk necessity to champion in intellect that the IBM manufacturer is a advertising obstruction when competing for mindshare with providers enjoy CrowdStrike, FireEye, Palo Alto Networks, and so forth. accordingly, IBM security necessity to work more durable and smarter to net the be alert out.
Many because of IBM for hosting me in San Francisco this week. I’ll be again on the Moscone middle for RSA within the blink of a watch.
IBM is enforcing a technique wherein in change for helping convey the advantages of blockchain to a growing checklist of industrial clients, it’s taking pictures seat and cloud revenues.
although, from its monetary reporting, it’s not practicable to examine how much of massive Blue’s exact line is attributable to its blockchain activities.
“demonstrate me” investors are left to invest and that i am supplementing their replete site in IBM with short-dated calls expiring simply after successive salary releases.
Over a 12 months and a half in the past, I wrote my first article about blockchain and massive Blue (IBM) entitled, “may This be IBM’s earnings Catalyst?” In it, i discussed a number of such initiatives including a world container transport blockchain led via Maersk (OTCPK:AMKBY), a fruit and vegetable consortium led by using Walmart (WMT), and a exchange finance blockchain involving quite a lot of alien banks, HSBC (HSBC) amongst them. These programs stand to bring wonderful – “sooner, enhanced, more cost-effective” – effects for his or her participants:
In that first article, I postulated that IBM would monetize blockchain from seat licenses not diverse to Bloomberg’s very successful model. In a comply with-up piece, I linked to an IBM page that established the validity of this inspiration with fees correlating to the measurement of the blockchain participant; larger groups paying greater, dinky ones gaining free access to the platform. essential mass and extensive participation are essential to multilateral processing represented by means of blockchain.enterprise keeps Coming
issues haven’t stood nonetheless. Blockchain initiatives involving IBM are proliferating straight away and in areas that I could not hold imagined in 2017. okay, so Abu Dhabi’s ADNOC oil blockchain turned into less complicated to foresee. however, past my realm of vision had been a healthcare blockchain and an additional having to upshot with scientific drug trials, blockchains for overseas change and derivatives, a gear to video panoply groundwater usage, yet another worried with mining, and a blockchain for digital advertising. infrequently a week goes through where they don’t notice recent blockchain initiatives spawned by using corporations desperate to work with IBM; at the least dozens of programs are underway involving a all bunch of major groups.but Its impact is Opaque
transport, meals, finance, oil, healthcare, medication, alien exchange, derivatives, water, mining, advertising – obviously it isn’t misplaced on any person that these are areas of big economic activity. For me, this makes it even more fascinating that nowhere of their numbers does IBM document earnings at once brought on by blockchain, neither with seat licenses, nor cloud revenues, nor cost-added services corresponding to AI. No, IBM has chosen to preserve blockchain-derived revenues opaque. They hold no passage of connecting the dots.
I don’t understand why IBM has assumed this posture. youngsters, as adversarial to obsessing about it, I actually hold chosen to flux on to the query, “Given this opaqueness coupled with the big edge significance of blockchain, how could one factor it into an IBM investment choice?” answer, actually: “with the aid of speculating, as in with acceptable bets that blockchain will power results in different areas for which IBM does account.”Speculating with Calls
in opposition t massive Blue’s mainly afflicted legacy of salary growth, cautious buyers say, “show me; point to it”. To this I add, “sure, however they don’t want to miss out if issues continue to be elevated character as they suspect they might.”
So, eventual month they begun an scan wherein I bought very short-dated at-the-cash calls commandeer earlier than IBM’s quarterly revenue free up. And, barring recent information or sparkling insights, I plot to upshot so once more next quarter and successively thereafter.
exceptionally, and with apologies for less than one data aspect, the morning before IBM’s end-of-day income announcement of January twenty second, for $3.20 I purchased calls expiring Feb 1st. now not 24 hours later, I closed out the contracts for $eight.sixty six realizing a profit of one hundred seventy%.
A side potential of buying and selling in IBM alternatives is that, in contrast to most others that expire but as soon as a month, IBM’s calls (and puts) are available with weekly expirations. This potential that one necessity not “pay for more time price” than is really warranted. If IBM’s salary are due out, say, on a fourth Tuesday, you can quiet purchase options expiring on that Friday; but pay attention as a result of things transpire right away.
options buying and selling is risky which is why brokerages pre-approve investors for it. As skilled as i am within the securities trade and as an investor, I simplest exchange alternate options a few times each and every yr when I actually hold a compelling hunch. furthermore, I try this company not through my discount broking service-broking, but somewhat, through their full-carrier B-D wherein I confer with a relied on and professional professional through the lifestyles-cycle of every trade. I certainly not wager greater than they are able to hold the funds for to lose and, on occasion, I lose it all, i.e. calls expiring nugatory. My adviser is alert of I hold in intellect and might ply the dangers.
it's obtrusive that IBM should re-set up its economic credibility by means of delivering consistent salary increase. Of the 22 analysts that commemorate the stock, wholly half hold it as a “dangle” with the community forecasting a median cost at about the site the stock is trading today. because it is with blockchain and other unique developments, I’m more confident than most in IBM which is why they are besides lengthy the inventory. That talked about, I wouldn’t intellect boosting their recrudesce which is why i will be able to pursue this call strategy for a long time.
Disclosure: i am/we are long IBM. I wrote this text myself, and it expresses my very own opinions. i'm not receiving compensation for it (apart from from seeking Alpha). I don't hold any commerce relationship with any enterprise whose inventory is outlined in this article.
further disclosure: always upshot your own due diligence in session with an authorized and able economic adviser who is alert your wonderful needs and puts your interests ahead of their own. bear in mind, there are added concerns in owning international securities including these associated with ADR sponsorship, purchasing and selling the pinks, overseas withholding taxes on dividends, and charges. (All my proceeds from contributing to SA fade to charity.)
In September 2018, IBM announced a recent product, IBM Db2 AI for z/OS. This synthetic intelligence engine displays facts access patterns from executing SQL statements, uses computer discovering algorithms to opt for top-quality patterns and passes this counsel to the Db2 question optimizer for expend via subsequent statements.desktop discovering on the IBM z Platform
In might besides of 2018, IBM introduced edition 1.2 of its desktop gaining knowledge of for z/OS (MLz) product. here's a hybrid zServer and cloud utility suite that ingests efficiency records, analyzes and builds models that characterize the health fame of a lot of symptoms, screens them over time and gives actual-time scoring capabilities.
a few aspects of this product providing are aimed at supporting a group of model builders and executives. as an example:
This computing device learning suite became at first aimed toward zServer-based mostly analytics purposes. probably the most first glaring decisions was zSystem efficiency monitoring and tuning. gadget administration Facility (SMF) statistics which are instantly generated by using the working device provide the uncooked facts for gear resource consumption reminiscent of material processor utilization, I/O processing, reminiscence paging etc. IBM MLz can assemble and rescue these facts over time, and construct and train fashions of device behavior, rating these behaviors, identify patterns now not effortlessly foreseen by means of people, help key performance warning signs (KPIs) after which feed the model consequences lower back into the gear to hold an upshot on gadget configuration changes that may enhance efficiency.
The subsequent step turned into to implement this suite to investigate Db2 performance facts. One answer, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) retort template, applies the computer gaining knowledge of expertise to Db2 operational facts to gain an knowing of Db2 subsystem fitness. it might probably dynamically construct baselines for key efficiency symptoms, supply a dashboard of these KPIs and provides operational cadaver of workers precise-time insight into Db2 operations.
whereas everyday Db2 subsystem performance is a crucial component in typical software health and performance, IBM estimates that the DBA aid cadaver of workers spends 25% or more of its time, " ... fighting entry route problems which reason efficiency degradation and repair affect.". (See Reference 1).AI comes to Db2
consider the plight of modern DBAs in a Db2 environment. In state-of-the-art IT world they must aid one or extra massive information purposes, cloud utility and database features, software installation and configuration, Db2 subsystem and utility performance tuning, database definition and management, catastrophe healing planning, and extra. query tuning has been in actuality on account that the origins of the database, and DBAs are usually tasked with this as neatly.
The coronary heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the records, experiences the places of the objects to be accessed and develops an inventory of candidate records entry paths. These access paths can involve indexes, desk scans, a number of desk connect strategies and others. within the data warehouse and big statistics environments there are continually extra selections obtainable. One of those is the actuality of abstract tables (once in a while called materialized question tables) that comprise pre-summarized or aggregated records, for this reason allowing Db2 to linger away from re-aggregation processing. one more election is the starjoin entry direction, habitual within the statistics warehouse, the site the order of table joins is changed for performance explanations.
The Optimizer then reviews the candidate access paths and chooses the access course, "with the bottom charge." imbue during this context skill a weighted summation of useful resource usage including CPU, I/O, reminiscence and different supplies. eventually, the Optimizer takes the lowest cost entry direction, outlets it in reminiscence (and, optionally, within the Db2 directory) and starts entry path execution.
large statistics and facts warehouse operations now consist of application suites that permit the enterprise analyst to expend a graphical interface to construct and exploit a miniature facts mannequin of the information they wish to analyze. The packages then generate SQL statements in keeping with the users’ requests.
The hardship for the DBA
to be able to upshot proper analytics in your diverse records retailers you necessity an excellent understanding of the statistics requirements, an realizing of the analytical services and algorithms available and a high-efficiency statistics infrastructure. lamentably, the quantity and placement of records sources is expanding (both in measurement and in geography), information sizes are starting to be, and purposes continue to proliferate in number and complexity. How should quiet IT managers attend this atmosphere, in particular with the most skilled and ripen cadaver of workers nearing retirement?
consider additionally that a sizable participate of cutting back the replete cost of possession of these techniques is to net Db2 functions to race faster and more successfully. This always interprets into using fewer CPU cycles, doing fewer I/Os and transporting much less information across the network. because it's regularly problematic to even identify which functions could improvement from performance tuning, one passage is to automate the detection and correction of tuning considerations. this is where computer studying and simulated intelligence will besides be used to awesome impact.Db2 12 for z/OS and simulated Intelligence
Db2 edition 12 on z/OS makes expend of the computer gaining knowledge of amenities mentioned above to assemble and champion SQL query textual content and entry path particulars, in addition to exact efficiency-connected historic information such as CPU time used, elapsed instances and influence set sizes. This offering, described as Db2 AI for z/OS, analyzes and outlets the facts in computing device getting to know fashions, with the mannequin evaluation results then being scored and made attainable to the Db2 Optimizer. The next time a scored SQL observation is encountered, the Optimizer can then expend the model scoring records as enter to its access route option algorithm.
The upshot should be a reduction in CPU consumption because the Optimizer uses mannequin scoring input to opt for greater entry paths. This then lowers CPU charges and speeds software response instances. a sizable knowledge is that the usage of AI software does not require the DBA to hold facts science talents or profound insights into question tuning methodologies. The Optimizer now chooses the finest entry paths based mostly now not best on SQL query syntax and information distribution facts but on modelled and scored historic performance.
This will besides be chiefly vital if you shop records in dissimilar places. as an instance, many analytical queries in opposition t huge records require concurrent entry to inevitable records warehouse tables. These tables are often known as dimension tables, and that they hold the statistics aspects continually used to control subsetting and aggregation. for example, in a retail ambiance agree with a table called StoreLocation that enumerates every shop and its site code. Queries against shop income records might besides wish to combination or summarize income with the aid of location; therefore, the StoreLocation desk can be used with the aid of some big statistics queries. in this environment it is ordinary to elevate the dimension tables and copy them regularly to the sizable information utility. in the IBM world this site is the IBM Db2 Analytics Accelerator (IDAA).
Now deem about SQL queries from each operational applications, statistics warehouse clients and massive statistics commerce analysts. From Db2's point of view, totality these queries are equal, and are forwarded to the Optimizer. besides the fact that children, within the case of operational queries and warehouse queries they should quiet certainly be directed to access the StoreLocation table in the warehouse. having said that, the question from the company analyst towards huge statistics tables should likely access the copy of the table there. This consequences in a proliferations of abilities access paths, and greater work for the Optimizer. fortunately, Db2 AI for z/OS can provide the Optimizer the information it needs to create smart entry course choices.how it Works
The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is frequently right here:
There are additionally various person interfaces that supply the administrator visibility to the repute of the accrued SQL commentary performance records and mannequin scoring.summary
IBM's machine learning for zOS (MLz) providing is getting used to outstanding impact in Db2 version 12 to help the efficiency of analytical queries in addition to operational queries and their linked functions. This requires administration attention, as you ought to investigate that your company is prepared to ingest these ML and AI conclusions. How will you measure the prices and advantages of the expend of computing device studying? Which IT guide team of workers ought to be tasked to reviewing the outcome of mannequin scoring, and maybe approving (or overriding) the results? How will you assessment and justify the assumptions that the application makes about entry direction decisions?
In different phrases, how neatly did you know your statistics, its distribution, its integrity and your current and proposed entry paths? this can determine the site the DBAs expend their time in assisting analytics and operational software performance.
# # #
John Campbell, IBM Db2 exotic EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 utility performance with computer researching"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-boost-ibm-db2-application-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
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IBM partners are gearing up for a year of increased enablement, champion and joint customer opportunities, as participate of deepened channel plans across Australia and recent Zealand.
According to Alison Freeman - universal manager of global commerce partners A/NZ at IBM - the vendor is doubling down on its partner ecosystem on both sides of the Tasman, amid plans to drive more software-as-a-service (SaaS) opportunities through the channel.
"Our priorities for this year are very limpid client segmentation, particularly from a software perspective on how and where they fade to market with their partners,” Freeman said. “There’s a sizable focus on embedded solution agreements - where partners are developing solutions with their technology.
"We’ve had some grand success and growth there in the past year. We’re getting the all commerce behind achieving double digit growth for their partners in 2019, that’s their focus."
Last year, sizable Blue wanted partners to hold a larger ply on commercial business opportunities, an district the vendor will continue working through with partners specific to software.
"There’s a lot of work going into that this year," added Freeman, when speaking to ARN on the sidelines of PartnerWorld at reflect in San Francisco. "We did that with a number of accounts eventual year, and this is a continuation.
"It’s a shift that allows partners to invest and bring on the right people and skills. We’re working with them by client, on which partners they shift, based on their skills. It’s a sizable stride and that’s how we’ll attend them grow, which will besides attend us grow."
Another point of focus for Freeman this year, is creating a better alignment with global system integrators (GSIs) such as KPMG, Wipro, HCL and DXC Technology.
This stride may raise some intrigue over the competitiveness between IBM and such GSIs, but Freeman said the vendor was looking at how it can strengthen its go-to-market strategy in other areas of the business.
Another particular district that IBM is concentrating on is centred around providing more enablement and skills for partners, Freeman said.
“We’re providing the tools to enable their partners in the very passage that they skill their IBM teams,” she said. “We’re pushing better enablement, better connection with their partners, protection under deal registration and much quicker pricing."
The tech giant formally rolled out recent initiatives, including its Watson-based collaboration engine called IBM commerce partner Connect - which A/NZ partners piloted eventual year; My PartnerWorld; recent software deal registration and IBM Power Systems, LinuxONE and Z ‘In It to Win It’ incentives.
In San Francisco, John Teltsch - partner ecosystem universal manager at IBM - revealed that partners created more than 84,000 opportunities and drove over US$14 billion in revenue during 2018.
Such growth was triggered by the migration of multiple tools to My Sales Activity (MySA), a lone portal that helps partners manage their commerce with the vendor.
Enhancements were besides made to the PartnerWorld on-boarding process, cutting the time to on-board recent partners by 70 per cent in the process, resulting in the addition of 13,000 net recent ecosystem partners globally in 2018.
“From a partner point of view, we’re seeing a lot of growth in security, SaaS solutions, as well as the Internet of Things," Freeman added.
"We’re very strong in those areas. From a systems point of view, we’re seeing growth in storage and that’s another district where we’ll behold to bring on recent partners."
Julia Talevski attended PartnerWorld at reflect in San Francisco as a guest of IBMJoin the newsletter!
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In September 2018, IBM announced a recent product, IBM Db2 AI for z/OS. This simulated intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to settle on optimal patterns and passes this information to the Db2 query optimizer for expend by subsequent statements.Machine Learning on the IBM z Platform
In May of 2018, IBM announced version 1.2 of its Machine Learning for z/OS (MLz) product. This is a hybrid zServer and cloud software suite that ingests performance data, analyzes and builds models that picture the health status of various indicators, monitors them over time and provides real-time scoring capabilities.
Several features of this product offering are aimed at supporting a community of model developers and managers. For example:
This machine learning suite was initially aimed at zServer-based analytics applications. One of the first obvious choices was zSystem performance monitoring and tuning. System Management Facility (SMF) records that are automatically generated by the operating system provide the raw data for system resource consumption such as central processor usage, I/O processing, reminiscence paging and the like. IBM MLz can collect and store these data over time, and build and train models of system behavior, score those behaviors, identify patterns not readily foreseen by humans, develop key performance indicators (KPIs) and then feed the model results back into the system to finger system configuration changes that can help performance.
The next step was to implement this suite to dissect Db2 performance data. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine learning technology to Db2 operational data to gain an understanding of Db2 subsystem health. It can dynamically build baselines for key performance indicators, provide a dashboard of these KPIs and give operational staff real-time insight into Db2 operations.
While universal Db2 subsystem performance is an essential factor in overall application health and performance, IBM estimates that the DBA champion staff spends 25% or more of its time, " ... fighting access path problems which reason performance degradation and service impact.". (See Reference 1).AI Comes to Db2
Consider the plight of modern DBAs in a Db2 environment. In today's IT world they must champion one or more sizable data applications, cloud application and database services, software installation and configuration, Db2 subsystem and application performance tuning, database definition and management, cataclysm recovery planning, and more. Query tuning has been in actuality since the origins of the database, and DBAs are usually tasked with this as well.
The heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the data, reviews the locations of the objects to be accessed and develops a list of candidate data access paths. These access paths can involve indexes, table scans, various table connect methods and others. In the data warehouse and sizable data environments there are usually additional choices available. One of these is the actuality of summary tables (sometimes called materialized query tables) that hold pre-summarized or aggregated data, thus allowing Db2 to avoid re-aggregation processing. Another option is the starjoin access path, common in the data warehouse, where the order of table joins is changed for performance reasons.
The Optimizer then reviews the candidate access paths and chooses the access path, "with the lowest cost." Cost in this context means a weighted summation of resource usage including CPU, I/O, reminiscence and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in reminiscence (and, optionally, in the Db2 Directory) and begins access path execution.
Big data and data warehouse operations now involve software suites that allow the commerce analyst to expend a graphical interface to build and exploit a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.
The Problem for the DBA
In order to upshot proper analytics on your multiple data stores you necessity a proper understanding of the data requirements, an understanding of the analytical functions and algorithms available and a high-performance data infrastructure. Regrettably, the number and location of data sources is expanding (both in size and in geography), data sizes are growing, and applications continue to proliferate in number and complexity. How should IT managers champion this environment, especially with the most experienced and ripen staff nearing retirement?
Understand besides that a big participate of reducing the total cost of ownership of these systems is to net Db2 applications to race faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could profit from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and simulated intelligence can be used to grand effect.Db2 12 for z/OS and simulated Intelligence
Db2 version 12 on z/OS uses the machine learning facilities mentioned above to assemble and store SQL query text and access path details, as well as actual performance-related historical information such as CPU time used, elapsed times and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the data in machine learning models, with the model analysis results then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then expend the model scoring data as input to its access path election algorithm.
The result should be a reduction in CPU consumption as the Optimizer uses model scoring input to pick better access paths. This then lowers CPU costs and speeds application response times. A significant edge is that using AI software does not require the DBA to hold data science skills or profound insights into query tuning methodologies. The Optimizer now chooses the best access paths based not only on SQL query syntax and data distribution statistics but on modelled and scored historical performance.
This can be particularly essential if you store data in multiple places. For example, many analytical queries against sizable data require concurrent access to inevitable data warehouse tables. These tables are commonly called dimension tables, and they hold the data elements usually used to control subsetting and aggregation. For example, in a retail environment deem a table called StoreLocation that enumerates every store and its location code. Queries against store sales data may wish to aggregate or summarize sales by Location; hence, the StoreLocation table will be used by some sizable data queries. In this environment it is common to elevate the dimension tables and copy them regularly to the sizable data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).
Now reflect about SQL queries from both operational applications, data warehouse users and sizable data commerce analysts. From Db2's perspective, totality these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely be directed to access the StoreLocation table in the warehouse. On the other hand, the query from the commerce analyst against sizable data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more work for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to create smart access path selections.How It Works
The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:
There are besides various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.Summary
IBM's machine learning for zOS (MLz) offering is being used to grand upshot in Db2 version 12 to help the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your commerce is prepared to consume these ML and AI conclusions. How will you measure the costs and benefits of using machine learning? Which IT champion staff must be tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the results? How will you review and justify the assumptions that the software makes about access path choices?
In other words, how well upshot you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs expend their time in supporting analytics and operational application performance.
# # #
John Campbell, IBM Db2 Distinguished EngineerFrom "IBM Db2 AI for z/OS: Boost IBM Db2 application performance with machine learning"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-boost-ibm-db2-application-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
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