MobileMiner: Mining Your Frequent Behavior Patterns On Your Phone Vijay Srinivasan, Saeed Moghaddam, Abhishek Mukherji, Kiran K. Rachuri, Chenren Xu, Emmanuel Munguia Tapia Advanced Software Platforms Lab Samsung Research America - Silicon Valley Replace samsung logo

Patterns From Your Phone “What are you doing now?” Detect Current Context “What do you typically do?” Mine Co-Occurrence Patterns “Which contexts typically happen around the same time?” “Typically, when I am home on Sunday nights, I call my parents” App Usage Activities and Places Lots of research in ubc comm on wot u r doing now Much faster Pre-condition Post-condition Association rules on multi-modal mobile context Call / SMS

Why Mine Co-Occurrence Patterns? Intuitive UI Pre-Loading Project Better explain pattern vs automation Show arrows one by one Automate frequent actions Users can review and select patterns to automate

Why Mine On the Phone? 2. Regions with no cloud or network access 1. Personal data privacy Network access? Think about population? 45 hours idle time per week on average Octa core & quad core processors 3. Powerful phone processors 4. Lengthy phone idle times

Outline MobileMiner system design Evaluation Is it feasible to mine patterns on the phone? How do we use co-occurrence patterns? Graying out Think about getting rid?

Rule Mining on Mobile Context Data Time Mobile Context Logs Support: Proportion of input context baskets in which a frequent itemset occurs Lowering support threshold increases mining time Morning Afternoon Home Work Jazz Confidence: P( post-conditions | pre-conditions ) CNN Call Bob Morning, Home Morning, Home, Jazz, CNN … Basket Extractor Context Baskets Morning, Home ( 10% ) Jazz, CNN ( 1% ) Afternoon, Work, Call Bob ( 5% ) … Frequent Itemset Miner Use milder black Frequent Itemsets Morning → Home ( 80% ) Jazz → CNN ( 90% ) Afternoon, Work → Call Bob ( 70% ) … Rule Generator Association Rules Key: ( Support , Confidence )

Optimized Apriori Algorithm Time Mobile Context Logs Morning Afternoon Home Work Jazz 1-3 months of data CNN Call Bob 𝑩 Morning, Home Morning, Home, Jazz, CNN … 𝑪 𝟏 ∗𝐁 Basket Extractor Context Baskets 𝑪 𝟐 ∗𝐁 𝑪 𝐧 ∗𝐁 … Optimized Apriori Algorithm Morning Home Gmail … Morning Home Jazz … Morning, Jazz Morning, Home Jazz, CNN … Morning, Home Jazz, CNN … Give some avg numbers for ck b … Candidate 1-itemsets 𝑪 𝟏 Frequent 1-itemsets Candidate 2-Itemsets 𝑪 𝟐 Frequent 2-Itemsets Frequent n-Itemsets 𝒌 (𝑪 𝒌 ∗𝐁) is expensive! ( 𝑩 𝒂𝒗𝒈 =𝟏𝟏𝟒𝟐𝟕𝟓, 𝑪 𝒌 ~ 𝟏𝟎−𝟏𝟎𝟎𝟎𝟎) More than 4 hours to mine patterns

Weighted Basket Mining Time Mobile Context Logs Morning Afternoon Home Work Jazz CNN Call Bob 𝑩 ′ Weighted Context Baskets Morning, Home (2%) Morning, Home, Jazz, CNN (0.2%) … 𝑪 𝟏 ∗ 𝑩 ′ 𝑪 𝟐 ∗ 𝑩 ′ 𝑪 𝐧 ∗ 𝑩 ′ Weighted Basket Extractor … Weighted Basket Mining Morning Home Gmail … Morning Home Jazz … Morning, Jazz Morning, Home Jazz, CNN … Morning, Home Jazz, CNN … Animate this better … Candidate 1-itemsets 𝑪 𝟏 Frequent 1-itemsets Candidate 2-Itemsets 𝑪 𝟐 Frequent 2-Itemsets Frequent n-Itemsets 𝑩 ′ << 𝑩 (92.5% size reduction) Mine patterns in 16.5 minutes ( 15 times improvement ) ( 𝑩′ 𝒂𝒗𝒈 =𝟖𝟓𝟓𝟗)

Context-Specific Mining Mobile Context Logs 1 minute Daily log 8 hours 6 hours Weighted Context Baskets Weighted Basket Extractor App Usage Baskets Outgoing Call Baskets … Filtering API Weighted Basket Mining Weighted Basket Mining Weighted Basket Mining (1/(24*60))*100 = 0.07% support 1% support 1% support 1% support Infeasible mining time of several days

Next Context Prediction Predicted Apps 92% Context Prediction Engine 90% Current Context Predicted Outgoing calls 80% 70% MobileMiner Association Rules Matching Rules Confidence Ranked app predictions → Outlook Add support value 70% 92% → 90% 90% 70% → 92% Prediction algorithm also ranks based on: Support value Number of pre-conditions in matching rule

Outline MobileMiner system design Evaluation Is it feasible to mine patterns on the phone? How do we use co-occurrence patterns?

Data Collection Details Mobile context data from 106 users 1-3 months of data from each user Start time & duration of context events 440 unique context events per user Call & SMS App usage Location Home, work, outside, … Getting rid of text under headings? Getting rid of status events Moving/not moving Charging Battery levels Location provider Moving / not moving Cell ID

Feasibility of Mining on the Phone Metrics Execution Time Memory CPU utilization Mobile Context Logs Weighted Context Baskets 1.4 seconds 11.6 MB 20.8% Basket Filtering 1.7 seconds 9.9 MB 22.9% Weighted Basket Extractor App Usage Baskets Weighted Basket Mining Weighted Basket Mining 16.5 minutes 44.2 MB 24.3% 21.2 seconds 1.0 MB 21.9% Combine extractor & mining? Remove energy? Just highlight 16.5 & 44.2 mb? Cpu utilization.. Rule Generation Rule Generation 1% support 1% support

Sample User Patterns & Potential Uses Post-conditions Put back animation Pre-conditions Charging or low power mode reminders Sensing place using cheaper attributes

Group Patterns & Potential Uses Post-conditions All 106 users Use color if time permits! Pre-conditions Bootstrapping with common patterns Group activity scheduling & recommendations

Next Context Prediction Accuracy App Prediction Service User preferences over 42 external users Tizen explain in 1st slide Tizen logo Explain numbers better Recall Precision Number of recommendations Similar trends for next contact prediction in paper

Conclusions Novel MobileMiner mobile system Weighted basket mining Context-specific mining Next context prediction Feasible to mine context patterns on the phone 16.5 minutes to mine 1-3 months of data Several applications of patterns Evaluated app & call prediction

Device Intelligence @ Samsung Research America Samsung Research America - Silicon Valley http://www.sisa.samsung.com/ ~1000 Researchers and Engineers 16 Labs (and growing!) Advanced Software Platform Lab Software innovations Core deliverables to Android and Tizen OS Open-Source Innovation Center Device Intelligence Group Core Expertise in Activity Recognition, Context-Awareness, Machine Learning, Mobile Computing, Device-Cloud coordination, Wearables University collaborations: Run MSM and find some interesting patterns. Cambridge CICESE Cornell EFPL Stanford Soft sensing Data Collection Crowd-sourced data Data fusion Mobile-Social

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