Updates to Astral page

db/lab.json

@@ -103,7 +103,7 @@
 		},
 		"Carl Nuessle" : {
 			"status" : "PhD",
-			"projects" : ["pocketdata"],
+			"projects" : ["pocketdata", "astral"],
 			"joint_advisor" : true,
 			"advisor" : ["Luke Ziarek"],
 			"pic" : {

db/people.json

@@ -1,4 +1,36 @@
 {  
+  "Carlos Bautista" : {
+    "institution" : "New York University",
+    "updated" : "2019"
+  },
+  "Chenjie Li" : {
+    "institution" : "Illinois Institute of Technology",
+    "updated" : "2019"
+  },
+  "Juliana  Freire " : {
+    "institution" : "New York University",
+    "updated" : "2019"
+  },
+  "Qitian Zeng" : {
+    "institution" : "Illinois Institute of Technology",
+    "updated" : "2019"
+  },
+  "Remi Rampin" : {
+    "institution" : "New York University",
+    "updated" : "2019"
+  },
+  "Sonia Castelo" : {
+    "institution" : "New York University",
+    "updated" : "2019"
+  },
+  "Sudeepa Roy" : {
+    "institution" : "Duke University",
+    "updated" : "2019"
+  },
+  "Zhengjie Miao" : {
+    "institution" : "Duke University",
+    "updated" : "2019"
+  },
   "Andrew Myers" : {
     "institution" : "Cornell University",
     "updated" : "2019"

db/publications.json

@@ -97,6 +97,17 @@
       "preprint" : "https://odin.cse.buffalo.edu/papers/2018/submitted/VLDB-LogCompression.pdf"
     }
   },
+  {
+    "title" : "Not Your Father's Big Data",
+    "authors" : ["Carl Nuessle"],
+    "venue" : "NEDB",
+    "year" : 2019,
+    "projects" : ["pocketdata", "astral"],
+    "length" : 2,
+    "urls" : {
+      "paper" : "https://odin.cse.buffalo.edu/papers/2010/NEDB-NotYourFathersBigData.pdf"
+    }
+  },
   {
     "title" : "Learning From Query-Answers: A Scalable Approach to Belief Updating and Parameter Learning",
     "authors" : [

lib/lab_metadata.rb

@@ -188,10 +188,15 @@ module LabMetadata
     )+"\n}"
   end
 
-  def LabMetadata.members_on_project(project)
-    $db["lab/members"].values.
-      where { |m| m.fetch("projects", []).include? project }.
-      map { |m| m["name"] }
+  def LabMetadata.members_on_project(project, role = /.*/)
+    role = case role
+            when :student then /PhD|BS|MS/ 
+            else role
+           end
+    $db["lab/members"].values
+      .where { |m| m.fetch("projects", []).include? project }
+      .select { |m| role =~ m.fetch("status", "") }
+      .map { |m| m["name"] }
   end
 
   def LabMetadata.alumni_on_project(project)

src/research/astral/index.erb

@@ -3,9 +3,9 @@ title: ASTral
 ---
 <img src="<%=asset_path("logos")%>/astral.png"  width="86" height="86" style="float: left; margin-top: 20px"/>
 
-<h1>ASTral</h1>
+<h1>ASTral / JITDss</h1>
 
-<p><b>Active Students:</b> <%= LabMetadata::members_on_project("astral").map { |m| LabMetadata::link_for(m) }.join(", ") %></p>
+<p><b>Active Students:</b> <%= LabMetadata::members_on_project("astral", :student).map { |m| LabMetadata::link_for(m) }.join(", ") %></p>
 
 <p>Recently, a swath of specialized data management systems has attempted to displace traditional relational databsaes, each sacrificing a measure of physical independence for the consequent performance gains. However, relying on an entire data management system built around a specific set of performance/capability tradeoffs requires making strong assumptions about (often unpredictable) workload expectations. ASTral does for specialized databse systems what self-describing data did for specialized schemas.  ASTral involves several sub-projects:</p>
 

src/teaching/cse-662/project_ideas.txt

@@ -1 +1,4 @@
 Probabilistic Schemas / Typechecker
+
+Possible worlds sketching -> Constraint management.
+  - If we can get a lower bound on whether a possible world belongs, we might be able to work out a system for greedy constraint solving (a'la what Guru was working on)