Future and Emerging Technologies (FET) Future and Emerging Technologies (FET) The roots of innovation Proactive initiative on: Global Computing (GC) Proactive initiative on: Global Computing (GC) DBGlobe IST rd Meeting Athens, November 29, 2002 UoI Presentation

Directories :: Resource Location Data Delivery Outline

Summaries for Resource Discovery Maintain summaries (e.g., Bloom filters) to assist the search for a service (resource) Directories for XML metadata and appropriate summaries Resource Discovery

Motivation: (DBGlobe) Large Scale and Dynamic Environment How to locate a resource System Model: Sites that store hierarchical descriptions of services (in XML) or XML documents Path queries Limitations (so far): We consider only XML-Trees (no cycles) No value queries Joint work with Georgia Koloniari

Resource Discovery device printer color postscript digital camera An example XML-description and the corresponding XML-tree Path queries From the root: //device/printer Partial: camera/digital * Overall Approach: maintain Bloom-based indexes to check whether a document (item) exists at a site (peer)

Resource Discovery Bloom-Filters Allocate a vector v of m bits, initially all set to 0 Choose k independent hash functions, h 1, h 2, …, h k, each with range {1,…, m}. For each element a  A, set the bits at positions h 1 (a), h 2 (a),..., h k (a) to 1. (A particular bit might be set to 1 multiple times) Given a query for b, check the bits at positions h 1 (b), h 2 (b),..., h k (b). If any is 0, then certainly b is not in the set A. Otherwise we assume that b is in the set (“false positive”). test if an element b exists in a set A = {a 1, a 2,…, a n } of n elements (keys) Element a h 1 (a) = P1 h 2 (a) = P2 h 3 (a) = P3 h 4 (a) = P4 m bits Bit Vector v

Breadth (or level) Blooms Resource Discovery The Breadth Bloom Filter (BBF) for an XML tree T with j levels: set of Bloom filters {BBF 0, BBF 1, BBF 2, … BBF i }, i ≤ j One Bloom filter, denoted BBF i, for each level i of the tree. BBF i : the labels (attributes) of all nodes at level i. BBF 0 : all attributes that appear in any node of the XML tree T. device printer color postscript digital camera { device, printer, camera, color, postscript, digital } {device} {printer, camera} {color, postscript, digital} BBF 0 BBF 1 BBF 2 BBF 3 The BBF i s are not of the same size We may skip levels

Depth (or Path) Blooms Resource Discovery The Depth Bloom Filter (DBF) for an XML tree T with j levels: set of Bloom filters {DBF 0, DBF 1, DBF 2, … DBF i-1 }, i ≤ j One Bloom filter, denoted DBF i, for each path of length i (with i+1 nodes) of the tree. DBF i : the labels (attributes) of all paths of length i. DBF 0 : all attributes that appear in any node of the XML tree T. device printer color postscript digital camera { device, printer, camera, color, postscript, digital } {device/printer, device/camera, printer/color, printer/postscript, camera/digital} {device/printer/color, device/printer/postscript, device/camera/digital DBF 0 DBF 1 DBF 2 Special symbol for “root” paths

Resource Discovery Preliminary performance results Both outperform (in terms of false positives) a same size simple bloom Depth (path) very sensitive on the number of levels Depth (path) need more space Updates are handled efficiently (just the corresponding vectors)

Distribution Each site:  local-filter: a bloom filter for local resources  one or more summary -filter summary-filter: merge of the bloom filters of a set X of other sites Resource Discovery

Horizons (keep information for up to horizon = d neighbors (as in routing indexes) A merged-filter for each path: merge of blooms for all sites on the path up to length equal to the horizon Resource Discovery Merged of nodes 1, Merged of nodes 3, Merged of nodes 6, 7, 8 0

Hierarchical Resource Discovery root peers Leaf sites : local filter Internal sites : summaries for all nodes in its subtree Root sites : summaries for other root sites

Resource Discovery Future work Evaluate distribution strategies Other ways of summarizing data (related work on selectivity estimation) See how this  can be related to ontologies (meaningful path queries)  whether/how it can be integrated with querying

Directories :: Resource Location Data Delivery Outline

A survey on different modes to transmit data:  Push/pull  Continuous (periodic) /a-periodic  Multicast/unicast  Directed diffusion (communication only with neighbor nodes) For the 1 st deliverable on the topic Data Delivery

The different data delivery modes in DBGlobe Tradeoffs of using one over the other (e.g., in registering services, directory (location updates) To be extended for D10 (Data Delivery and Querying) For the 1 st deliverable on the topic Data Delivery

Data Delivery Modes and Coherence Data Delivery Focus: How to achieve temporal (currency) and Semantic (transaction-based) Coherency of Data under different modes of data delivery

The Data Broadcast Model Client Server Broadcast Channel The server broadcasts data from a database to a large number of clients push mode + no direct communication with the server Data updates at the server Periodic updates for the values on the channel Data Delivery  Efficient way to disseminate information to large client populations with similar interests  Physical support in wireless networks (satellite, cellular)  Alternative way of transmitting information for data intensive applications (e.g., web)

 Multiple Versions: Not just one value per item, but k such values [Pitoura&Chrysanthis, IEEE TC 2003]  Temporal and Semantic Coherency (Theory and Protocols) [Pitoura,Chrysanthis&Ramamritham, ICDT03] Data Delivery Clients must read consistent and current data without contacting the server directly

Currency  (x, u)  RS(R) CI(x, R) Currency Interval of an item x in RS(R) - CI(x, R) - is [c b, c e ) where c b is the time instance when the value was stored in the database, c e is the time insatnce of the next change of this value in the database  , say [c b, c e ) overlapping- equal to c e - RS(R) is a subset an actual database state at the server older value OV_Currency(R) = c e -, where c e is the smallest among the right limits of CI(x, R) Data Delivery Currency Interval for a set (readset) Two properties: Temporal spread (discrepancies among database states) Temporal Lag (how old with regards some point in time (e.g., T_commit)

Protocols and their properties  Timestamps (versioning)  Invalidation Reports  Propagation Data Delivery

Consistency Degrees of Consistency C0 C1 RS(R)  DS C2 R serializable with the set of server transactions that read values read (directly or indirectly) by R C3 R serializable with the all server transactions C4 R serializable with the all server transactions and the serial izability order of the server transactions that R observes is consistent with the commit order of transactions at the server Data Delivery

Protocols and their properties Data Delivery Relation to temporal coherency Based on broadcasting the serialization graph of the server (or parts of it)

Future Work Multiple servers model Applications in sensor networks Data Delivery

DBGlobe IST