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The document is a bar chart showing the progress of 3 series across 4 categories. Series 1 has the lowest percentages across all categories, peaking at 30% in Category 3. Series 2 is higher than Series 1 in all categories and peaks at 60% in Category 4. Series 3 is the highest overall, reaching 100% in Category 4.
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This chart shows the distribution of 4 categories across 3 data series. Category 1 makes up 0-10% for Series 1, Category 2 is 10-30%, Category 3 is 30-60%, and Category 4 is 60-100%. The y-axis shows the percentage allocation for each category across the 3 data series.
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This bar graph compares the performance of three different series across five categories, with series 1 performing the best in categories 1 through 3, series 2 performing the best in category 4, and series 3 trailing the other two series in all categories.
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This chart shows the performance of three different series over time in percentages from 0% to 100%. Series 1 started lower than the others but ended higher than Series 2 and 3. While Series 2 performed consistently in the middle, Series 3 had the highest performance early on but tapered off later.
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The document contains a bar chart comparing three data series. Series 1 represents the lowest values, Series 2 is higher than Series 1 but lower than Series 3, which shows the highest values ranging from 0% to 100%.
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The document is a chart showing the progress of three different series over time from 0% to 100% in 10% increments. Series 1 reached 100% the fastest while Series 2 and 3 progressed at a slower rate with Series 3 being the slowest to reach completion.
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This document contains two data series plotted on a graph. Series 1 shows increasing values from 0% to 100% in 20% increments. Series 2 is not labeled and does not contain any data points, showing that it is an empty data series. The graph compares the two series but does not provide any other context or labels.
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This bar graph compares three series of data, with each series represented by a different color bar. Serie 1 shows a steady decline from 100% to 0% in evenly spaced increments. Serie 2 plateaus at 80% for the first several increments before declining to 0% at a faster rate than Serie 1. Serie 3 rises to 70% before declining to 0% at the slowest rate of the three series.
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This bar chart compares three data series across four categories. Series 1 is the lowest across all categories, peaking at around 60% in Category 3. Series 2 exceeds Series 1 in every category, reaching a high of 90% in Category 2. Series 3 towers over the other two series, hitting 100% in Category 1 and remaining at or above 80% in the other three categories.
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This chart shows the distribution of 4 categories across 3 data series. Category 1 makes up 0-10% for Series 1, Category 2 is 10-30%, Category 3 is 30-60%, and Category 4 is 60-100%. The y-axis shows the percentage allocation for each category across the 3 data series.
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This bar graph compares the performance of three different series across five categories, with series 1 performing the best in categories 1 through 3, series 2 performing the best in category 4, and series 3 trailing the other two series in all categories.
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This chart shows the performance of three different series over time in percentages from 0% to 100%. Series 1 started lower than the others but ended higher than Series 2 and 3. While Series 2 performed consistently in the middle, Series 3 had the highest performance early on but tapered off later.
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The document contains a bar chart comparing three data series. Series 1 represents the lowest values, Series 2 is higher than Series 1 but lower than Series 3, which shows the highest values ranging from 0% to 100%.
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The document is a chart showing the progress of three different series over time from 0% to 100% in 10% increments. Series 1 reached 100% the fastest while Series 2 and 3 progressed at a slower rate with Series 3 being the slowest to reach completion.
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This document contains two data series plotted on a graph. Series 1 shows increasing values from 0% to 100% in 20% increments. Series 2 is not labeled and does not contain any data points, showing that it is an empty data series. The graph compares the two series but does not provide any other context or labels.
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This bar graph compares three series of data, with each series represented by a different color bar. Serie 1 shows a steady decline from 100% to 0% in evenly spaced increments. Serie 2 plateaus at 80% for the first several increments before declining to 0% at a faster rate than Serie 1. Serie 3 rises to 70% before declining to 0% at the slowest rate of the three series.
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This bar chart compares three data series across four categories. Series 1 is the lowest across all categories, peaking at around 60% in Category 3. Series 2 exceeds Series 1 in every category, reaching a high of 90% in Category 2. Series 3 towers over the other two series, hitting 100% in Category 1 and remaining at or above 80% in the other three categories.
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The document is a bar chart with 4 categories on the x-axis and percentages on the y-axis. It contains 3 data series which show the breakdown of data across the 4 categories. Series 1 is the highest for Category 1 and lowest for Category 4, while Series 2 and 3 show different breakdowns across the categories.
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This chart shows the percentage breakdown of four categories, with three data series represented. Category 1 makes up 0-10% for the first series, 10-20% for the second, and 20-30% for the third. The remaining categories have progressively higher percentage ranges up to 100% for the final data point in each series.
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The document is a bar chart with 4 categories on the x-axis and percentages on the y-axis. It contains 3 data series which show the breakdown of data across the 4 categories. Series 1 is the highest for Category 1 and lowest for Category 4, while Series 2 and 3 show different breakdowns across the categories.
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The graph shows the percentage of goals scored by different players in a soccer game. Miki scored the most goals at around 40%, while asysty assisted on the most goals at around 30%. The other players miki2, miki3 and miki4 scored progressively less goals, with miki4 scoring the least at about 10%.
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Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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