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think about, you're capable of finding a person in a crowd; you have by no means viewed him earlier than, you don't know his precise name; only that the one is doubtless there. likely there but hiding. Hiding behind fictitious id, fake name. All this can also be uncovered with the solution described below.
devil lies in the element. faucet into new depths and probabilities with new strategies discovering effective information it is hidden in unstructured statistics. Even with a state of the art in superior hyperlink analysis or records visualization tools at one’s disposal, the “full” photo lacks crucial tips whether it is based mostly only on element of records attainable, like discovering hidden entities in paragraphs of electronic conversation or determining fictitious entities with of IBM Watson and IBM i2 applied sciences.
An amount of statistics for legislations Enforcement investigation rises daily. it is almost overwhelming, primarily records in unstructured kind. The entertaining DATERA’s method combines structured and unstructured information for a better understanding and thorough perception into the data that companies already possess.
Even with a fine talents of i2 products family unit, there are nevertheless techniques a way to enrich investigation statistics for a more robust, and greater accurate result. concerning i2 capabilities, the visualization of entities and hyperlinks hidden in unstructured information, is specially challenging. For an unstructured information analysis, an IBM Watson technology is used to observe and to find entities and their relations in undeniable text or speech modified to text. moreover, fake identities and fictitious entities are uncovered using an IBM identity perception. one in all components protected in the IBM identity perception is an IBM InfoSphere world name administration which helps control, search, analyze, and examine multicultural name datasets through leveraging lifestyle-particular identify records, and linguistic guidelines.
because of that, it is possible to find and detects additionally non-obtrusive relationships between entities and identities and make connections which are otherwise hidden in the noise. This formulation additionally enriches the existing intelligence discoveries with highlighting particulars that investigator could no longer note earlier than.
The whole idea is in accordance with a true LEA customer. because of a sensitivity of the customer’s facts a concrete use case become modelled on a well-known enterprise called Enron. As an input, they employed these publicly available Enron connected records:
and applied these steps:
regarding i2 centralized repository, this transform elementary tables and uncooked statistics into actual-life mannequin of entities and connections. Prerequisite for here is careful design of entity mannequin and mapping of data to this model. All analyzed and changed statistics are loaded into this repository. this is important because the quantity of communications is awfully huge and if analyst would analyze best few in the community opened mailboxes this method can cause missing connections and conversations. typical manner is displayed on following schema.
When all evaluation, transformations and records hundreds are finished, they will see naturally visualized assistance like:
illustration visualization chart that includes also entities detected with the aid of Watson can be seen on the screen beneath.
Chart with connections isn't the only way the way to visualize stored statistics. All links and communications may also be projected to a heatmatrix. This category of representation shows you at what period of time became the communication most active, if the dialog part changed into despatched in inappropriate time or even suspicious time. The more conversations were despatched, the darker the colour gets. via this means analyst can see these spots instantly.
a different way how to refine time dimension is displaying entities and connections in a timeline. every entity gets its own define on the chart and all connections are represented one at a time ordered by time sequence.
every exploration begins with simply few entities and after a while which you can prolong your center of attention deeper into the facts community. restricted search in response to each attribute or standard increasing the context of particular entity will support you to get full view in one area.
It’s been practically precisely 10 months due to the fact that IBM formally launched its analytics-infused messaging gadget known as Verse, and whereas the enterprise’s announcement of “utility for a brand new solution to work” firstly generated a comparatively wonderful blast of press and analyst insurance, buzz concerning the cloud-first product has diminished given that. So I went in the hunt for indications of exhilaration, and even signs of existence, for Verse on the IBM connect convention this week in Orlando.
It’s fair to claim that IBM didn’t precisely lead with Verse in terms of its IBM join marketing, fairly pushing a broader message about social business and digital event. notwithstanding a fresh press unencumber touting the enterprise’s “becoming social company momentum” does point out Verse a couple of instances and even cites one of IBM’s Connections Cloud purchasers –- an expert functions and telesales company known as Flex Contact center -- this is now rolling out Verse to eleven,000 personnel global. What’s greater, the integrated mail and calendaring expertise turned into the area of a large number of classes, from “Going cellular with IBM Verse” to “boom – Deploying IBM Verse and Connections Cloud at IBM.”
DESIGN considering: How end-person guilt inspires IBM to enrich your e-mail eventIBM
IBM Verse cloud-primarily based mail gadget
It took a long time for IBM to get round to Verse (as in, brief for “converse”) on Monday on the demonstrate, which kicked off that day with an appearance by means of charged-up/arduous NatGeo tv show host Jason Silva and an amusing recycled TED@IBM speak on why computing devices deserve to have a sense of humor. certainly, the primary huge point out of Verse on the adventure came all the way through one of those seemingly limitless demos starring IBM personnel pretending to be valued clientele.
all through the showcasing of a homepage that gifts a unified view of mail, calendaring, social networks and notifications, IBM client journey govt Chris Crummey -- styling the infrequent combination pocket square/iPhone case -- employed Verse to attain out to a colleague and accelerate sharing of content. He also confirmed how Verse’s calendaring technology can use Watson-powered smarts to suggest him about which of a couple of conflicting conferences he should attend (uh, the one with probably the most individuals above him within the org chart) and display screen emails for tone. moreover, Crummey established how a Verse calendar image may also be taken in order that it can also be accessed offline.Bob Brown/NetworkWorld
IBM's Chris Crummey stays in sync with colleagues by the use of his mobile phone all over a social company demo at IBM connect
also all over the outlet sessions, enterprise Social solutions GM Jeff Schick dropped a bombshell -- smartly, ok, some thing the component is that’s somewhat under a bombshell –-that IBM can be presenting Verse as an on-premises improve to its Domino “subsequent” social enterprise utility platform right through the 2d half of 2016. yes, if you have not been paying attention, both the Domino server and Notes client -- formerly the celebrities of IBM join predecessor Lotusphere -- do live. The thinking goes that there's nevertheless demand for on-premises items by way of these agencies in all probability nevertheless now not reasonably comfortable with inserting everyting in the cloud as well as those now not allowed to achieve this because of governance guidelines.
Schick, who has been a part of IBM's collaboration business for the reason that 2006 and is the daddy of IBM Connections social company network platform, improved on Verse right through a observe-up interview.
He boasted that it is the first internet-based mostly mail with an encrypted local keep for offline use. it's also the first messaging device using true faceted search, in response to the Apache Solr engine, that permits superior sleuthing for information, Schick says. Verse too can support clients visualize who all those people are that get cc:ed on messages despatched to you.
"There are advancements that you just could make to mail and calendaring that are only so intuitive," he says. "there's loads of innovation that may in fact take area in this area."Bob Brown/NetworkWorld
IBM business Social options GM Jeff Schick reveals Toscana, a conversation engine that could bolster Verse and other commercial enterprise social tools
for instance, Schick discussed a cell app and net-primarily based dialog engine dubbed Toscana that might work with items like Verse and could handle shortcomings of widespread interior text messaging-like apps similar to Slack, which he claims users are challenged with from an guidance company standpoint. "individuals are combating the simple utility of having a conversation and how to get returned to the advice and decisions," he referred to, adding that it's uncertain no matter if Toscana can be offered as a separate product or embedded in Verse or Connections, or both. (it is slated to go into client beta in April.)
(right here's the way to trial Verse in case you have any need.)
IBM’s Verse team dove deeper into the e mail offering in a breakout session all through which the product’s 2016 highway map become revealed. A sampling of
April 1, 2019 Michael Sansoterra
As I accomplished populating some check tables with a large extent of records on a small and transient IBM i partition within the cloud, i believed life was first rate. but my countenance fell as i spotted the tables plus OS hogged over 70 % of the disk space. I wondered how to get all the data into a single retailer file for safe protecting.
The buzzer in my mind become loud and clear: it ain’t gonna work, you don’t have enough room. As I loathed the idea of using dissimilar retailer data to save my examine information, I remembered that most keep file instructions have a data compression (DTACPR) parameter. I never used it so I determined to are attempting it with retailer Library (SAVLIB) to see how neatly it worked. I performed SAVLIB with DTACPR(*excessive) and i become pleased that the compression changed into first rate adequate to let me save the complete check library with about 7 percent storage to spare on the device.
IBM offers three compression alternate options (*LOW, *MEDIUM and *excessive) and proven below is how IBM’s documentation describes each and every option (the emphasis is mine):
These are all older compression algorithms, and i had only heard of LZ1.
I determined to go back and compare the purchasable compression alternate options. I used the shop object (SAVOBJ) command to store an 8GB customer table into a store file as follows:SAVOBJ OBJ(consumer) LIB(MYDATA) DEV(*SAVF) OBJTYPE(*FILE) SAVF(QGPL/MYSAVF) CLEAR(*replace) DTACPR(*NO)
I cleared and re-used the identical save file (SAVF) with each look at various. The outcomes are shown within the table below with the adaptations of the information compression option:DTACPR alternative Avg CPU % utilizationSAVOBJ length SAVF measurement (bytes) % of normal size *NO 4% 5:forty one 8774656000 *excessive forty% 13:eleven 5687762944 64.eight% *MEDIUM 33% 10:46 5701132288 sixty five.0% *LOW 14% three:forty two 6383755264 72.eight%
This test turned into finished on a Power9 cloud partition operating IBM i 7.three with two vCPUs, 4GB of RAM and 200GB of disk.
The average CPU% utilization in the chart isn’t a excessive precision metric, it turned into really me eye-balling the work with gadget activity (WRKSYSACT) command and observing the regular CPU utilization over time. besides the fact that the gadget wasn’t doing a good deal anyway these store assessments, there is still some CPU charge to run everything. This computer multiple between .5 % and 1.5 p.c while “idle”. the vast majority of the CPU was definitely due to the compression operation.
The chart demonstrates that it may also be quite costly when it comes to CPU to request *excessive or *MEDIUM compression degrees, even though admittedly this computer handiest had 2 vCPUs. nevertheless, you definitely would need to be certain your gadget has satisfactory CPU means before operating a retailer command (SAVnnn) with one of those compression alternate options.
For my consumer table, there wasn’t plenty house reductions between *excessive and *MEDIUM compression (simplest about .2 p.c). while the *LOW alternative wasn’t as effective in space discounts (by means of about eight percent compared to *excessive), it performed the quickest out of the entire strategies. If time is of the essence, beware, as you can see the *excessive and *MEDIUM alternatives took fairly somewhat longer than a shop without compression.
Of course your effects might also range, depending on how conducive your records objects are to compression. statistics with many repetitive features typically compresses neatly. Admittedly, my test “consumer” table had a bunch of random characters in it. So, the odds are you can also are expecting a more robust compression ratio for “normal” facts.
I determined to do a secondary test to peer how neatly “compressible” statistics reminiscent of a large undeniable textual content file would do. I downloaded the free checklist of great Britain postal codes from the Geonames.org web page. I unzipped it to /tmp/GB_full.txt on the IBM i and used the keep object (SAV) command to shop this text file from the IFS to a shop file.SAV DEV('/qsys.lib/qgpl.lib/mysavf.file') OBJ(('/tmp/GB_full.txt')) CLEAR(*substitute) DTACPR(*excessive)
This chart includes the numerous keep file sizes reckoning on the selected facts compression (DTACPR):File Description File dimension (bytes) % of fashioned sizeUncompressed dossier173821160 Zip file (usual down load) 13946129 8.0% keep file-no compression 184705024 106.three% save file-high compression 18907136 10.9% shop file-medium compression 32538624 18.7% retailer file-low compression 173039616 99.6%
I didn't include length or CPU% for this examine, because the elapsed time of the save operation wasn’t massive. I’m blissful I did this examine as a result of this result is reasonably a bit diverse from the first examine with recognize to how smartly the quite a few compression tiers performed.
Zip compression became the clear winner compared to the IBM i’s older compression algorithms. bear in mind, which you could use the jar command the use of QSHELL for zipping/unzipping IFS information. if you don’t mind shopping the information superhighway, a couple of utilities and different compression formats (together with 7z and tar) can even be used from QSHELL to compress IFS info, if getting tremendous size reduction or sharing facts and not using a retailer file is your primary purpose. If mandatory, you could at all times place the zip file right into a retailer file to have the best of both worlds!
unlike the first compression demo, there changed into reasonably slightly of difference between the resulting file sizes for the distinct compression forms. Whereas *LOW compression turned into fairly constructive in the prior check, with the simple text file *LOW completed pretty much nothing.
In conclusion, when saving facts to a retailer file, it can pay to experiment to gauge the cost (CPU utilization and period) vs improvement (disk space savings) of using a selected records compression option. Don’t overlook, the optimal settings will depend upon your records set (for instance, program objects vs desk statistics and journal receivers, plain textual content records vs binary records, etc.) so remember to test for every adaptation. if you’re only worried with compressing IFS information, then other compression options can be found.connected STORY
keep Object (SAVOBJ)
vigour programs no longer Getting 3D XPoint memory anytime quickly The look for intelligent existence interestingly Is Over
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Background Automated telephone communication systems (ATCS) can deliver voice messages and collect health-related information from patients using either their telephone's touch-tone keypad or voice recognition software. ATCS can supplement or replace telephone contact between health professionals and patients. There are four different types of ATCS: unidirectional (one-way, non-interactive voice communication), interactive voice response (IVR) systems, ATCS with additional functions such as access to an expert to request advice (ATCS Plus) and multimodal ATCS, where the calls are delivered as part of a multicomponent intervention. Objectives To assess the effects of ATCS for preventing disease and managing long-term conditions on behavioural change, clinical, process, cognitive, patient-centred and adverse outcomes. Search methods They searched 10 electronic databases (the Cochrane Central Register of Controlled Trials; MEDLINE; Embase; PsycINFO; CINAHL; Global Health; WHOLIS; LILACS; Web of Science; and ASSIA); three grey literature sources (Dissertation Abstracts, Index to Theses, Australasian Digital Theses); and two trial registries (www.controlled-trials.com; www.clinicaltrials.gov) for papers published between 1980 and June 2015. Selection criteria Randomised, cluster- and quasi-randomised trials, interrupted time series and controlled before-and-after studies comparing ATCS interventions, with any control or another ATCS type were eligible for inclusion. Studies in all settings, for all consumers/carers, in any preventive healthcare or long term condition management role were eligible. Data collection and analysis They used standard Cochrane methods to select and extract data and to appraise eligible studies. Main results They included 132 trials (N = 4,669,689). Studies spanned across several clinical areas, assessing many comparisons based on evaluation of different ATCS types and variable comparison groups. Forty-one studies evaluated ATCS for delivering preventive healthcare, 84 for managing long-term conditions, and seven studies for appointment reminders. They downgraded their certainty in the evidence primarily because of the risk of bias for many outcomes. They judged the risk of bias arising from allocation processes to be low for just over half the studies and unclear for the remainder. They considered most studies to be at unclear risk of performance or detection bias due to blinding, while only 16% of studies were at low risk. They generally judged the risk of bias due to missing data and selective outcome reporting to be unclear. For preventive healthcare, ATCS (ATCS Plus, IVR, unidirectional) probably increase immunisation uptake in children (risk ratio (RR) 1.25, 95% confidence interval (CI) 1.18 to 1.32; 5 studies, N = 10,454; moderate certainty) and to a lesser extent in adolescents (RR 1.06, 95% CI 1.02 to 1.11; 2 studies, N = 5725; moderate certainty). The effects of ATCS in adults are unclear (RR 2.18, 95% CI 0.53 to 9.02; 2 studies, N = 1743; very low certainty). For screening, multimodal ATCS increase uptake of screening for breast cancer (RR 2.17, 95% CI 1.55 to 3.04; 2 studies, N = 462; high certainty) and colorectal cancer (CRC) (RR 2.19, 95% CI 1.88 to 2.55; 3 studies, N = 1013; high certainty) versus usual care. It may also increase osteoporosis screening. ATCS Plus interventions probably slightly increase cervical cancer screening (moderate certainty), but effects on osteoporosis screening are uncertain. IVR systems probably increase CRC screening at 6 months (RR 1.36, 95% CI 1.25 to 1.48; 2 studies, N = 16,915; moderate certainty) but not at 9 to 12 months, with probably little or no effect of IVR (RR 1.05, 95% CI 0.99, 1.11; 2 studies, 2599 participants; moderate certainty) or unidirectional ATCS on breast cancer screening. Appointment reminders delivered through IVR or unidirectional ATCS may improve attendance rates compared with no calls (low certainty). For long-term management, medication or laboratory test adherence provided the most general evidence across conditions (25 studies, data not combined). Multimodal ATCS versus usual care showed conflicting effects (positive and uncertain) on medication adherence. ATCS Plus probably slightly (versus control; moderate certainty) or probably (versus usual care; moderate certainty) improves medication adherence but may have little effect on adherence to tests (versus control). IVR probably slightly improves medication adherence versus control (moderate certainty). Compared with usual care, IVR probably improves test adherence and slightly increases medication adherence up to six months but has little or no effect at longer time points (moderate certainty). Unidirectional ATCS, compared with control, may have little effect or slightly improve medication adherence (low certainty). The evidence suggested little or no consistent effect of any ATCS type on clinical outcomes (blood pressure control, blood lipids, asthma control, therapeutic coverage) related to adherence, but only a small number of studies contributed clinical outcome data. The above results focus on areas with the most general findings across conditions. In condition-specific areas, the effects of ATCS varied, including by the type of ATCS intervention in use. Multimodal ATCS probably decrease both cancer pain and chronic pain as well as depression (moderate certainty), but other ATCS types were less effective. Depending on the type of intervention, ATCS may have small effects on outcomes for physical activity, weight management, alcohol consumption, and diabetes mellitus. ATCS have little or no effect on outcomes related to heart failure, hypertension, mental health or smoking cessation, and there is insufficient evidence to determine their effects for preventing alcohol/substance misuse or managing illicit drug addiction, asthma, chronic obstructive pulmonary disease, HIV/AIDS, hypercholesterolaemia, obstructive sleep apnoea, spinal cord dysfunction or psychological stress in carers. Only four trials (3%) reported adverse events, and it was unclear whether these were related to the interventions. Authors' conclusions ATCS interventions can change patients' health behaviours, improve clinical outcomes and increase healthcare uptake with positive effects in several important areas including immunisation, screening, appointment attendance, and adherence to medications or tests. The decision to integrate ATCS interventions in routine healthcare delivery should reflect variations in the certainty of the evidence available and the size of effects across different conditions, together with the varied nature of ATCS interventions assessed. Future research should investigate both the content of ATCS interventions and the mode of delivery; users' experiences, particularly with regard to acceptability; and clarify which ATCS types are most effective and cost-effective.
... In India, adherence has been variably associated with socio-demographic characteristics (Cauldbeck et al., 2009;Rai et al., 2013;Safren et al., 2005;Sarna et al., 2008), but more consistently with psychosocial factors. Social support has been associated with higher adherence ( Nyamathi et al., 2012;Shah et al., 2007), and depression is linked with poorer adherence ( Anuradha et al., 2013;Nyamathi et al., 2012;Sarna et al., 2008). Although stigma has been associated with poor adherence in many settings ( Kingori et al., 2012;Rao, Kekwaletswe, Hosek, Martinez, & Rodriguez, 2007;Rintamaki, Davis, Skripkauskas, Bennett, & Wolf, 2006;Vyankandondera et al., 2013) and noted as a barrier in qualitative studies (Joglekar et al., 2011;Kumarasamy et al., 2005), it has not been quantitatively assessed in India. ...
... Self-report adherence measures are typically used in non-research contexts, but most have been developed in Western settings ( Simoni et al., 2006). The Adult AIDS Clinical Trials Group scale (AACTG; Chesney et al., 2000) and a single-item visual analog scale (VAS; Amico et al., 2006) have both been used in India (Ekstrand, Chandy, Heylen, Steward, & Singh, 2010;Ekstrand et al., 2011;McMahon et al., 2013;Shah et al., 2007;Venkatesh et al., 2010;Walshe et al., 2010) and two studies have compared the measures with each other McMahon et al., 2013). However, both were conducted in private hospitals and may not be generalizable to non-clinical settings. ...
... Contrary to their hypotheses, they observed no association between stigma and adherence. The single-item AACTG measure has been frequently used in India ( Anuradha et al., 2013;Ekstrand et al., 2010;Shah et al., 2007;Walshe et al., 2010) and correlates relatively well with viral load. Previous reports of adherence using this measure range from 74% to 97%, consistent with the 93% they observed. ...
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