Patent Publication Number: US-2011055221-A1

Title: Method and apparatus for obtaining decision diagrams from unknown hash identifiers

Description:
BACKGROUND 
     Service providers (e.g., wireless and cellular services) and device manufacturers are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services and advancing the underlying technologies. One area of interest has been in ways to reduce data traffic on the existing networks while maintaining a level of service acceptable to users. Search queries and the results of search queries have substantially increased congestion on networks. The number of systems and platforms performing a search query using a decision diagram is increasing. Such a decision diagram is used to organize data in a search query into a tree-type data structure that permits identification of a result by traversing various branches of the structure. As users continue to increase their reliance on data retrieved from networks, the number of search queries and the results of search queries transmitted in decision diagram form also increase. Consequently, service providers and device manufacturers face the challenge of providing sufficient communication and network resources to support queries based on or related to decision diagrams. 
     SOME EXAMPLE EMBODIMENTS 
     According to one embodiment, a method comprises receiving a hash identifier that is computed based on a reduced ordered binary decision diagram constructed for a resource description framework graph. The method also comprises determining whether the received hash identifier matches a predetermined one of a plurality of hash identifiers. The method further comprises reconstructing or querying for the decision diagram corresponding to the received hash identifier, when there is no match. 
     According to another embodiment, an apparatus comprising at least one processor, and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to receive a hash identifier that is computed based on a reduced ordered binary decision diagram constructed for a resource description framework graph. The apparatus is also caused to determine whether the received hash identifier matches a predetermined one of a plurality of hash identifiers. The apparatus is further caused to reconstruct or query for the decision diagram corresponding to the received hash identifier, when there is no match. 
     According to another embodiment, a computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to receive a hash identifier that is computed based on a reduced ordered binary decision diagram constructed for a resource description framework graph. The apparatus is also caused to determine whether the received hash identifier matches a predetermined one of a plurality of hash identifiers. The apparatus is further caused to reconstruct or query for the decision diagram corresponding to the received hash identifier, when there is no match. 
     According to another embodiment, an apparatus comprises means receiving a hash identifier that is computed based on a reduced ordered binary decision diagram constructed for a resource description framework graph. The apparatus also comprises means for determining whether the received hash identifier matches a predetermined one of a plurality of hash identifiers. The apparatus further comprises means for reconstructing or querying for the decision diagram corresponding to the received hash identifier, when there is no match. 
     Still other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings: 
         FIG. 1  is a diagram of a system capable of reducing communications traffic on networks by querying unknown hash identifiers (“hash IDs”), according to one embodiment; 
         FIG. 2  is a diagram of the components of a hash identifier application according to one embodiment; 
         FIG. 3  is a flowchart of a process for obtaining decision diagrams from unknown hash identifiers, according to one embodiment; 
         FIGS. 4A-4B  are diagrams of a binary decision diagram and a corresponding reduced ordered binary decision diagram, according to one embodiment; 
         FIG. 5  is a partial diagram of an external index table utilized in the process of  FIG. 3 , according to one embodiment; 
         FIG. 6  is a flowchart for handling hash identifiers, according to one embodiment; 
         FIG. 7  is diagram of a social network utilized in the process of  FIG. 3 , according to one embodiment; 
         FIG. 8  is a diagram of a smart space structure, according to one embodiment; 
         FIG. 9  is a diagram of hardware that can be used to implement an embodiment of the invention; 
         FIG. 10  is a diagram of a chip set that can be used to implement an embodiment of the invention; and 
         FIG. 11  is a diagram of a mobile terminal (e.g., handset) that can be used to implement an embodiment of the invention. 
     
    
    
     DESCRIPTION OF SOME EMBODIMENTS 
     A method and apparatus for obtaining decision diagrams from unknown hash identifiers are disclosed. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It is apparent, however, to one skilled in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention. 
     As used herein, the term “decision diagram” refers to a compact graphical and/or mathematical representation of a decision situation, sets, or relations. A decision diagram, for example, may be a binary decision diagram (BDD) or a reduced ordered binary decision diagram (ROBDD). A BDD is “ordered” if different variables appear in the same order on all paths from the root. A BDD is “reduced” if any isomorphic subgraphs of its graph are merged and any nodes whose two child nodes are isomorphic are eliminated. Isomorphic subgraphs of the same decision diagram have similar appearance but originate from different sources. An ROBDD is a group of Boolean variables in a specific order and a directed acyclic graph over the variables. A directed acyclic graph (DAG) contains, for instance, no cycles. This means that if there is a route from node A to node B then there is no way back from node B to node A. Although the term BDD almost always refers to an ROBDD, this application refers to ROBDD separately from BDD to avoid confusion. 
     A decision diagram may be used to organize any data, including search queries, into a tree-type data structure that permits identification of a result by traversing various branches of the structure. Although various embodiments are described with respect to search queries, it is contemplated that the approach described herein may be used with other data that can be organized into a tree-type data structure. The term “AugBDD” refers to an augmented ROBDD which is augmented information including the ROBDD and at least one of a header with a hash ID, a construction history of the ROBDD, and cardinality information (e.g., relationships between data tables, constraints on the types and number of class instances a property may connect with respect to a given ontology, etc.). 
     As used herein, the term “construction history information” of a hash identifier of interest includes at least one or more other hash identifiers corresponding to a respective one or more other decision diagrams used to construct a decision diagram corresponding to the hash identifier of interest. The construction history also includes identification of one or more Boolean operators applied to the other hash identifiers listed in history. 
       FIG. 1  is a diagram of a system capable of reducing communications traffic on networks by obtaining decision diagrams from unknown hash identifiers. As discussed above, the use of decision diagrams for organizing data in search queries is growing. However, this growth can also potentially increase demand on network resources for transmitting the queries and corresponding search results within the network. The system  100  of  FIG. 1  addresses this problem by transmitting a hash identifier representing a decision diagram rather than the decision diagram itself, thereby reducing network traffic. More specifically, the system  100  provides for hash tables listing known or existing decisions diagrams along with their corresponding respective hash identifier and other related information. An entity performing a query comprising one or more hash identifiers or receiving query results including one or more hash identifiers may then consult one of the hash tables to obtain the corresponding decision diagrams. 
     In certain cases however, a received or transmitted hash identifier may not correspond to a known or otherwise available decision diagram. In other words, the hash identifier is unknown with respect to the receiving entity. For example, upon receiving a hash identifier corresponding to a decision diagram that is not known or retained by the receiving entity, the entity needs to obtain a complete decision diagram corresponding to the received hash identifier by another mechanism. The system  100  enables the receiving entity to obtain the decision diagram by reconstructing the decision diagram from the hash identifier and accompanying construction history. Because reconstruction can occur locally at the receiving entity, reconstruction advantageously reduces demand on network resources. Moreover, the system  100  enables the flexibility to obtain the decision diagram by either reconstructing the decision diagram or requesting the decision diagram from the entity that sent the hash identifier via a query. In one embodiment, the determination of whether to reconstruct or query for the decision diagram can be based on an evaluation of available network resources, computational resources, and the like. 
     By way of example, the system  100  is applied to freely formed resource description framework (RDF)-graphs or RDF-graphs generated by ontology based information sharing systems over the semantic web. In one embodiment, RDF graphs represent decision diagrams and describe resources with classes, properties, and values. Sets of properties are defined within RDF Vocabularies (or Schemas). A node/resource is any object which can be pointed to by a uniform resource identifier (URI), properties are attributes of the node, and values can be either atomic values for the attribute, or other nodes. RDF Schema provides a framework to describe application-specific classes and properties. Classes in RDF Schema are like classes in object oriented programming languages. This allows resources to be defined as instances of classes, and subclasses of classes. The RDF graphs are represented or encoded in decision diagrams which describe the properties and relations of different classes. For example, information about a particular web page (a node), includes the property “Author”. The value for the Author property could be either a string giving the name of the author, or a link to a resource describing the author. One typical example of an rdfs:Class is foaf:Person in the Friend of a Friend (FOAF) vocabulary. An instance of foaf:Person is a resource linked to the class using an rdf:type predicate, such as in the following formal expression “John rdf:type foaf:Person” of the natural language sentence. 
     Ontology has notions of a property/attribute, which has a range and a domain (both of which define classes). A class has a name and potentially several associated properties, and it may be a subclass of another class. A class can be instantiated to a graph so that it is represented by a node in the graph. Possible properties are represented as arcs from one class node to other class nodes. These property-arcs can be properties of the object which have values (that are the nodes targeted by the property arcs). For instance, in person ontology, a Person class has a name property and an ID property, which places a restriction on the domains of both properties: e.g., define both properties as a Person class. Constraints may be added on the range of properties. For example, the range of ID is a number (a data value) and the range of name is another class, such as FullName. A class instance typically also has an arc that connects it to its class type. 
     A query can be performed against the originally received query result decision diagram, using criteria included in the query instruction, to generate a second query result decision diagram. In this regard, the second query result decision diagram can be a subset of the originally received query result decision diagram. To generate the second query result decision diagram, various logical operations, such as the logical-and operation, can be utilized. The query result data associated with this subsequent query may be decoded using the same dictionary that was used to decode the originally received query result decision diagram. The query result data can also be output to a user. Further, subsequent queries may also be performed, that further narrow the results, in the same manner. 
     Each RDF-graph includes a set of unique triples in a form of subject, predicate, and object, which allow expressing graphs. For example, in this piece of information “Jenna is Matti&#39;s friend,” the subject may be Jenna, the predicate may be friend, and the object may be Matti. The simplest RDF-graph is a single triple. Any node or entity can store unconnected graphs. As later explained in more detail, the approach described herein can be adapted in a smart space that includes the semantic web and has distributed nodes and entities that communicate RDF-graphs (e.g., via a blackboard or a shared memory). 
     The smart space is interoperable over different information domains, different service platforms, and different devices and equipment. For example, the smart space accommodates transmission control protocol/Internet protocol (TCP/IP), Unified Protocol (UniPro) created by the Mobile Industry Processor Interface (MIPI) Alliance, Bluetooth protocol Radio Frequency Communication (RFCOMM), IPv6 over Low power Wireless Personal Area Networks (6LoWPAN), etc. The smart space also covers technologies used for discovering and using services, such as Bluetooth/human interface device (HID) services, web services, services certified by the Digital Living Network Alliance (DLNA), the Network on Terminal Architecture (NoTA). In addition, the smart space constitutes an infrastructure that enables scalable producer-consumer transactions for information, and supports multiparts, multidevices and multivendors (M3), via a common representation of a set of concepts within a domain and the relationships between those concepts, i.e. ontologies. The smart space as a logical architecture has no dependencies on any network architecture but it can be implemented on top of practically any connectivity solution. Since there is no specific service level architecture, the smart space has no limitation in physical distance or transport. 
     The smart space allows cross domain searches and provides a uniform, use case independent service application programming interface (API) for sharing information. As an example, the smart space allows a mobile platform to access contextual information in, e.g., a car, home, office, football stadium, etc., in a uniform way and to improve the user experience, without compromising real-time requirements of the embedded system. The smart space uses an ontology governance process as the alternative to using case-specific service API standardization. The ontology governance process agrees and adopts new vocabularies using Resource Description framework (RDF) and RDFS (RDF schema). When RDFS is not sufficient for defining and instantiating the ontologies, web ontology language (OWL) or the like is used. 
     In one embodiment, the RDF is used to join data from vocabularies of different domains (such as business domains), without having to negotiate structural differences between the vocabularies. In addition, the RDF allows the smart space to merge the information of the embedded domains with the information in web, as well as to make the vast reasoning and ontology theory, practice and tools developed by the semantic web community available for developing smart space applications. The smart space is an aggregation of individual smart spaces of private, group or public entities and the smart space makes the heterogeneous information in embedded domains available for semantic web tools. The smart space architecture expands the concept of a deductive closure towards a distributed deductive closure. The smart space architecture addresses values in application development by abolishing the need for a prior use case standardization such as those in the Digital Living Network Alliance (DLNA) domain and the Bluetooth domain. Furthermore, the smart space architecture abolishes design time freezing of the address of any used service API, such as in the case of WebServices. 
     The smart space architecture is different from university-driven RDF-store based approaches in getting information of embedded systems as an integral part of the search extent. The space-based approach of the smart space architecture also provides an alternative to surrendering personal data to a search engine or a service provider. The smart space architecture applies to the semantic web an end-to-end design principle which is widely applied in the Internet, since communication media can never know the needs of endpoints as well as the endpoints themselves. 
     The smart space architecture allows a user&#39;s devices purchased at different times and from different vendors to work together. For example, the user can listen/watch/etc. to music/movies/etc. and have the sound output directed to a set of high quality speakers and/or display whenever the user is using a personal device in the vicinity of the high quality speakers/display. The smart space architecture allows application developers to mash-up services in different domains, instead of trying to port one application to all platforms and configurations. The smart space architecture allows device manufacturers to make interoperable products, so that consumers have no concerns about compatibility of different products and accessories. 
     Each individual smart space within the smart space architecture can be constructed by physically distributed information stores. For example, the personal information of a family is stored at home linked with one information store, while it is augmented with non-personal information at a website (e.g., a social networking website) linked with the same or a different information store. In this example, the website operator prefers augmenting rather than merging the information due to, for instance, copyright and/or privacy concerns. 
     One of the problems of sharing information in the semantic web is to share the graphs or parts of the graphs (i.e., subgraphs) among distributed nodes and entities via information stores with sufficient identification of the graphs (especially the subgraphs) while minimizing communication traffic. 
     To address this problem, a system  100  of  FIG. 1  introduces the capability to reduce communications traffic on networks by utilizing existing hash identifiers. The RDF graphs can be encoded to decision diagrams to be communicated between the nodes and entities. To further reduce communication traffic, the system  100  encodes (e.g., hashes) the decision diagrams into hash IDs, and avoids sending decision diagrams by sending the hash IDs and optionally a construction history of the decision diagrams. By way of example, a reduced ordered binary decision diagram (ROBDD) is used as an efficient representation for a binary decision diagram representing an information set and hashed with a hash function into a hash identifier (hash ID). ROBDD is essentially a group of Boolean variables in a specific order and a directed acyclic graph over the variables. A depth-first search of the ROBDD yields all possible values of the information set described by the ROBDD. 
     Each ROBDD is constructed from a binary decision diagram (representing a set of bits and their relationship) by means of reduction rules. The basic logical operations: and, or, not, equivalence, existential and universal abstractions are defined for ROBDDs as reduction rules. In other words, the system  100  constructs a new ROBDD by means of logical operations over a BDD. The constructed ROBDD is canonical for the set of bits it represents and for the order of its variables. The order of variables affects the size of the constructed ROBDD. 
     From a constructed ROBDD, the system  100  obtains a possible solution to the logical formula the ROBDD represents by traversing the ROBDD. This can be done in polynomial time (i.e., the running time is upper bounded by a polynomial in the size of the input for the algorithm). The problem of finding the best variable ordering is NP-hard (NP stands for Nondeterministic Polynomial time), i.e., inherently difficult to provide algorithms that are efficient for both general and specific computations. Since the problem of finding a satisfying assignment to variables in a logical formula is known to be NP complete (the class of NP-complete problems contains the most “difficult” problems in NP), the construction of the ROBDD is difficult. However, in practice ROBDDs have proved to be a very efficient way of encoding and operating on large sets, although it may be challenging to find a satisfying assignment of variables in a logical formula to construct a ROBDD. 
     As shown in  FIG. 1 , the system  100  comprises a user equipment (UE)  101   a  having connectivity to a personal computer  101   b , a web service platform  103   a  and a communication platform  103   b  via a communication network  105 . Each of the UE  101   a , the personal computer  101   b , the web service platform  103   a  and the communication platform  103   b  has a hash identifier application  107  and a database  109  for storing hash identifier and decision diagram information. By way of example, the communication network  105  of system  100  includes one or more networks such as a data network (not shown), a wireless network (not shown), a telephony network (not shown), or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), satellite, mobile ad-hoc network (MANET), and the like. 
     The UE  101   a  is any type of mobile terminal, fixed terminal, or portable terminal including a mobile handset, station, unit, device, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, Personal Digital Assistants (PDAs), or any combination thereof. It is also contemplated that the UE  101   a  can support any type of interface to the user (such as “wearable” circuitry, etc.). 
     By way of example, the UE  101   a , the personal computer  101   b , the web service platform  103   a  and the communication platform  103   b  communicate with each other and other components of the communication network  105  using well known, new or still developing protocols, such as Smart Space Access Protocol (SSAP). In this context, a protocol includes a set of rules defining how the network nodes within the communication network  105  interact with each other based on information sent over the communication links. The protocols are effective at different layers of operation within each node, from generating and receiving physical signals of various types, to selecting a link for transferring those signals, to the format of information indicated by those signals, to identifying which software application executing on a computer system sends or receives the information. The conceptually different layers of protocols for exchanging information over a network are described in the Open Systems Interconnection (OSI) Reference Model. 
     Communications between the network nodes are typically effected by exchanging discrete packets of data. Each packet typically comprises (1) header information associated with a particular protocol, and (2) payload information that follows the header information and contains information that may be processed independently of that particular protocol. In some protocols, the packet includes (3) trailer information following the payload and indicating the end of the payload information. The header includes information such as the source of the packet, its destination, the length of the payload, and other properties used by the protocol. Often, the data in the payload for the particular protocol includes a header and payload for a different protocol associated with a different, higher layer of the OSI Reference Model. The header for a particular protocol typically indicates a type for the next protocol contained in its payload. The higher layer protocol is said to be encapsulated in the lower layer protocol. The headers included in a packet traversing multiple heterogeneous networks, such as the Internet, typically include a physical (layer 1) header, a data-link (layer 2) header, an internetwork (layer 3) header and a transport (layer 4) header, and various application headers (layer 5, layer 6 and layer 7) as defined by the OSI Reference Model. 
       FIG. 2  is a diagram of the components of the hash identifier application  107   a , according to one embodiment. By way of example, the hash identifier application (e.g., a widget)  107   a  includes one or more components for querying unknown hash identifiers. Widgets are light-weight applications, and provide a convenient means for presenting information and accessing services. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In this embodiment, the hash identifier application  107   a  includes a control logic  201  for controlling the operation of the hash identifier application, and a reconstructing module  203  for: (1) reconstructing a decision diagram, when there is no hash identifiers matching with a received hash identifier, and (2) reconstructing a decision diagram based on a hash identifier extracted form an ID column or a history column of an external index table. The hash identifier application  107   a  also includes a querying module  205  for querying for a reduced ordered binary decision diagram or querying for unknown hash identifier(s). The hash identifier application  107   a  further includes an determining module  207  for determining (1) whether a received hash identifier matches a predetermined one of a plurality of hash identifiers and (2) whether to reconstruct or query for the decision diagram by comparing a cost of reconstructing with a cost of querying, and the hash identifier and decision diagram database  109   a . The hash identifier applications  107   b ,  107   c ,  107   d  have the same or similar features of the hash identifier application  107   a.    
       FIG. 3  is a flowchart of a process for obtaining decision diagrams from unknown hash identifiers, according to one embodiment. In one embodiment, the hash identifier application  107   a  performs the process  300  and is implemented in, for instance, a chip set including a processor and a memory as shown  FIG. 10 . In step  301 , the hash identifier application  107   a  receives a hash identifier that is computed based on a reduced ordered binary decision diagram constructed for a resource description framework graph. The hash identifier application  107   a  then determines whether the received hash identifier matches a predetermined one of a plurality of hash identifiers (Step  303 ). Thereafter, the hash identifier application  107   a  reconstructs or queries for the decision diagram corresponding to the received hash identifier, when there is no match (Step  305 ). 
       FIGS. 4A-4B  are diagrams of a binary decision diagram  400  and a corresponding ROBDD  420  utilized in the processes of  FIG. 3 , according to various embodiments. Various ways may be used to convert an RDF graph into the representation of, for example, a BDD or an ROBDD. In one embodiment, general BDD encoding is based on creating a triple (a, b, c) in which pieces of information “a,” “b,” and “c” are represented using three bits per each piece of information. This encoding scheme results in a triple represented, for instance, as (101, 001, 011), that is in turn maintained in a dictionary, e.g., as (a=101, b=011, c=011). In another embodiment, the number of bits used for encoding is calculated based upon the size of an associated letter. In another embodiment, the number of bits used for encoding is a set value such as 32 or 64 bits. BDD encoding can be accomplished by performing logic-OR operations with each bit sequence associated with a query result. 
     By way of example, the system  100  constructs a BDD to encode the triple. For instance, the BDD gets the following variable assignment for the nine variables: v1 &amp; ˜v2 &amp; v3 &amp; ˜v4 &amp; ˜V5 &amp; v6 &amp; ˜7 &amp; v8 &amp; v9 (“BDD_prev”). Each variable is a bit. The satisfying variable assignment to this BDD is precisely the encoding of the above-mentioned triple. The constructed BDD is a graph and is serialized to a chosen format to be fed to a hash function. 
     The success of the system  100  relies on the uniqueness of ROBDDs. For example, a BDD constructed for a given information set is unique for a chosen variable order. An ROBDD independently constructed with the same variable order for the same information set is always the same over the semantic web or the smart space. The system  100  keeps an internal index of constructed ROBDDs. The values of the internal index depend on the order of local ROBDD constructions. 
     In addition, the system  100  creates an external index table (a “hash table”; see  FIG. 5  for more details) in which each constructed ROBDD is given an ID (hereinafter “hash ID”) by operating a hash function over its ROBDD graph structure. Ideally, the hash function would never produce the same hash ID for two different ROBDDs. The system  100  maintains the external index table to store each new, unique ROBDD in a column  540 .  FIG. 5  is a partial diagram of an external index table utilized in the process of  FIG. 3 , according to one embodiment. This table further contains owner and access control information in a column  520  as well as the construction history of the ROBDD in a column  530 . 
     The “access control” column  520  in the external index table may be a simple implementation of allowing/disallowing access, or may be used as a link to an access control system of the underlying architecture, such as the credentials used in the semantic web. In one embodiment, hash 101  is accessible for all, while hash 26  is accessible only for Matti. Since hash 31  is set as accessible for all group members, only group members can reconstruct a ROBDD corresponding to hash 31  based on the construction history (e.g., BDD_OR(hash 26 ,hash 101 )) of the ROBDD, since only group members have a decryption key from Matti regarding hash 37  and hash 12 . If a non-group member receives hash 31  and the construction history, the non-group member does not have sufficient information to reconstruct ROBDD corresponding to hash 31  based on the construction history because the non-group member does not have the appropriate key. In another embodiment, the control access function sends only hash IDs while using some other mechanisms to communicate the corresponding ROBDD. 
     The “history” column  530  shows that a new ROBDD can be constructed by an internal BDD core operation bdd_apply (which constructs the BDDs based on different operations: and, or, not, implication, forall, exists, xor, if-then-else and their possible combinations). The new ROBDD is then hashed to form a hash ID, and this hash ID is maintained in the external index table in a column  510 . As shown in  FIG. 5 , an ROBDD corresponding to hash  31  can be constructed or reconstructed by applying a BDD operation (e.g., BDD_OR) on the ROBDDs corresponding to hash IDs (e.g., hash 26 , hash 101 ) listed in the construction history. 
     Since the ROBDD may be constructed by BDD operations from other ROBDDs, the system  100  sends a succinct representation of the ROBDD by sending this history of BDD operations (i.e., the construction history of the ROBDD) along with a hash ID. Sending only the succinct representation (e.g., hash ID) of the ROBDD and history reduces potential data traffic especially in cases where the construction of the ROBDD has been performed based on general components that are expected to be commonly available and unchanging (such as a representation of an ontology or typical search for (Matti, a, person) which constitutes a partial ROBDD). The construction history may be complete or partial (up to a parameterized number). 
     For each incoming ROBDD graph, the system  100  repeats the same operations of generating a hash ID and storing the hash ID and a corresponding construction history in the external index table (e.g., hash table). For each incoming hash ID, the system  100  searches in the external indexing table for the incoming hash ID. When a matching hash ID is found in the ID column of the external index table, the system  100  associates the received hash ID with the ROBDD graph corresponding to the marched hash ID. Otherwise, the system  100  requests a sending party to send the ROBDD graph corresponding to the received hash ID or missing information for reconstructing the ROBDD graph. In one embodiment, this implementation can be added to the smart space, or built on top of the smart space. 
     The processes for handling incoming hash IDs are shown diagrammatically in  FIG. 6 , in various embodiments. The first scenario is that the received hash ID is found in the ID column of the external indexing table (Step  601 ). The system  100  uses the received hash ID from the sending party, and proceeds directly to retrieve the ROBDD graph from the external index table or perform any other action relating to the ROBDD (Step  605 ). 
     The second scenario is that the received hash ID is not found in the ID column of the external indexing table (Step  601 ) and the hash ID does not exist in the construction history of the external indexing table (Step  602 ). The system  100  requests the corresponding ROBDD graph of the AugBDD from the sending party (Step  606 ), and then proceeds to other actions (Step  605 ). 
     The third scenario is either that the hash ID exists in the construction history of the external indexing table (Step  602 ), or the hash ID (e.g., hash 31 ) is received with the construction history (e.g., BDD_OR(hash 26 ,hash 101 )) of the ROBDD (i.e., [optional history]). When the available construction history (optionally including received construction history) is determined as complete or sufficiently complete enough (e.g., all hash ID(s) involve in the construction history of the unknown ROBDD have complete entries in the external indexing table) (Step  603 ), the system  100  reconstructs the ROBDD from its construction history and the associated hash ID(s). The system  100  saves the reconstructed ROBDD graph in the external indexing table (Step  604 ) and then proceeds to other actions (Step  605 ). 
     In another scenario, the system  100  may determine that the construction history is available but is not complete (e.g., does not identify all hash IDs or Boolean operations needed to reconstruct the ROBDD). For example, some other hash IDs (e.g., hash 37 , hash 12 ) involved in the construction history (via hash 26 ) of the unknown ROBDD have no complete entries in the external indexing table (Step  603 ). As such, the unknown ROBDD corresponding to the hash ID (e.g., hash 31 ) cannot be constructed from the available construction history (optionally including received construction history) due to the missing hash ID(s): hash 37 , hash 21 . 
     The system  100  then determines whether to try to reconstruct the unknown ROBDD (Step  607 ). As a receiving party, the system  100  has a choice to request any missing hash ID(s) included in the construction history of the received hash ID to enable reconstructing the unknown ROBDD, or to request the unknown ROBDD graph corresponding to the hash ID. For example, the decision may depend on whether the system  100  wants to use its own computing resources over network resources. The choice can be parameterized based on internal resource and network conditions, i.e., described in terms of parameters including available computational resources, available network bandwidth, internal and network loading, reconstruction costs, querying costs, or a combination thereof. However, if the relevant information is not available, the choice is heuristic (i.e., experience-based, such as a rule of thumb). 
     If deciding to query for the unknown ROBDD, the system  100  queries for the corresponding ROBDD graph of the AugBDD from the sending party (Step  606 ), and then proceeds to other actions (Step  605 ). When choosing to request the unknown ROBDD graph, the sending party can either send the ROBDD graph, or not to send it in order to push the system  100  to construct the ROBDD graph itself. In another embodiment, the sending party communicates only a part or parts of the ROBDD graph so as to distribute the workload between itself and the system  100 . Both the sending partying and the system  100  can freely turn off the sending or accepting of hash IDs and the consecution history, and communicate full ROBDD graphs instead. In other words, the history information allows the sending party and the system  100  to divide the AugBDDs and to recompose them from partly existing sources, which can be used as means of delegating computation to the parties. 
     If deciding to reconstruct the unknown ROBDD, the system  100  queries for the missing hash ID(s) from the sending party (Step  608 ), reconstructs the ROBDD from the available construction history and the involved hash IDs (including received missing hash ID(s)), and saves the reconstructed ROBDD graph in the external indexing table (Step  604 ), and then proceeds to other actions (Step  605 ). When the system  100  chooses to request the missing hash ID(s), it shoulders the computation and network resources and cost to reconstruct the ROBDD. 
     The embedding of an ROBDD in an AugBDDs means that the ROBDD graph is converted to some representation in known ways. This representation is then augmented with a header (or postfix) which contains the hash ID and a section describing the construction history of the ROBDD. The system  100  can choose any encoding for this, such as a simple implementation that writes out the hash IDs and operations in a reverse Polish notation (“RPN”) which is a mathematical notation wherein every operator follows all of its operands. An example of this notation is: “ab 123   34   cd   3  AND  23   dfg  OR.” 
     This example expresses (1) taking two ROBDDs represented by hash IDs ab 123 ,  34   cd   3 , (2) performing the BDD AND operation on them, and then (3) using that result to OR with an ROBDD represented by a hash ID  23   dfg . In another example, the system uses only one hash ID instead of the two ANDed hash IDs. 
     For adding the implementation to the smart space, the system  100  augments the smart space protocol with a specific get_graph message which has the hash ID as a parameter and which is a return message to get the actual ROBDD graph representation. Alternatively, the get_graph message triggers another smart space message (recursively, if necessary) to get the complete ROBDD graph. 
     In practice, the system  100  accepts a hash function with a sufficiently low probability of producing same hash IDs for different ROBDDs. The hash function can be parameterized so that the resulting hash IDs have a very high probability to be unique. Instead of communicating representations of ROBDD graphs, the system  100  communicates the hash IDs. The receiving party can then compare the hash IDs with the hash IDs stored in the receiving party&#39;s external index table. For those received hash IDs that are unknown to the receiving party (i.e., which are not found in the external index table of the receiving party), the receiving party can request the actual ROBDD graphs to be sent thereto. The receiving party then updates its external index table to contain the hash IDs and the corresponding ROBDD graphs. 
     In one embodiment, the hash function operates one-way such that the information of the ROBDD graph cannot be reconstructed based only on the hash ID. As such, the system  100  builds an access control mechanism for the hash IDs. For example, certain hash IDs are not allowed to be communicated with particular parties or not allowed at all to be communicated externally. In another embodiment, the system  100  further tracks whether a ROBDD graph or other information has been sent out and whether the information has been sent via a potentially insecure channel or network. 
     The “Final Technical Report—Specification Of A Security Architecture For Distributed Terminals” (“Final Technical Report,” incorporated herein by reference in its entirety) published by the Information Society Technologies in November 2002 (p. 40-41) described on pages 40-41 estimates about the probability of the collision of keyed hashes (Message Authentication Codes—MACs), when the key was fixed. The Final Technical Report mathematically proved collision resilience of the keyed hashes. Instead of MACs, the system  100  generates hash IDs and reduces a probability of identical hash IDs for different ROBDDs to be as low as possible. The Final Technical Report used a Reed-Solomon based hash function by first applying a one-way hash function (such as SHA-1) to the data and then inputting the outputs from the hash function to a Reed-Solomon code, to provide sufficient security. In one embodiment of the invention, the hash function is composed of a one-way hash function and a Reed-Solomon based hash function. In some other embodiments, the hash functions do not need to be composed with a Reed-Solomon based hash function. Well known Reed-Solomon codes are rather long with very high minimum distance. If IDs are 4 hexadecimal digits long, the probability for two different ROBDDs to have the same ID is approximately 2̂-12. By increasing the length of the IDs to 5 hexadecimal digits long, the probability becomes approximately 2̂-17. The Final Technical Report assumed that 128-bit truncated SHA-1 provides a sufficient security level. This approach keeps a low probably of ROBDD collision without considerably increasing the key length or the length of the hash IDs. This means that if ROBDD 1  is given, it is extremely difficult (in cryptographic sense) to find another ROBDD 2  such that f(ROBDD 1 )=f(ROBDD 2 ). To further reduce the possibility of providing two different ROBDDs with the same hash ID, the system  100  deploys SHA-256, which means that hash IDs are 256-bit long. Using suitable truncation of a hash function provides decent length hash ID. For example, 16-bytes long hash IDs sufficiently ensure that it is nearly impossible to create ROBDD 2  such that ID_BDD 1 =ID_BDD 2  and that the occurrence of communicating an ambiguous hash ID is very unlikely. 
     The system  100  sends and receives hash IDs (i.e., hash values of ROBDDs), instead of the ROBDDs. Since the hash values are shorter than the values representing the ROBDD graphs, data traffic is significantly reduced at an acceptable rate of false positives. In another embodiment, short hash values (e.g., truncated hash values) are used, when there is a large amount of ROBDDs. For a RDF graph containing a large amount of data, the system  100  may process only a part or parts of the RDF graph that are feasible to be constructed as an AugBDD. 
     For efficiency reasons, the system  100  uses hash IDs that are as short as possible. Rather than using the hash IDs generated based upon a standard hash function like SHA-1, the system  100  truncates the hash IDs, for example to 128-bits. In another embodiment, the system  100  uses another standard hash function implementation such as SHA-256, and truncates the results. As discussed, 128-bit is adequate. Nevertheless, if truncating the hash IDs into lower values, other countermeasures may be adapted in order to prevent the forgery of the ROBDDs. When security and privacy are secured via other means, the system  100  only needs to ensure that two ROBDDs do not accidentally collide with each other, and 64-bit truncation is adequate. The implementation of the truncation size can be parameterized. By way of example, the simple Reed-Solomon based hash function implementation described in the Final Technical Report is used. 
     In one embodiment, the system  100  is adapted to a social network. Considering a situation shown in  FIG. 7 , users (e.g., Jenna and Matti) want to participate in the social network with their mobile terminals  710 ,  720 . In other embodiments, the users participate via personal computers or different kinds of devices or equipment. In this example, both Jenna and Matti have information that is public (such as their moods) and information that is private (such as their bank account balances). The private and public information are constructed as AugBDDs and given unique hash IDs. The AugBDDs can be combined to form more complete information of the users and given another unique hash ID. 
     The social network has friendship relations and public information of its participants. These relations and information are formed into RDF graphs, which can be constructed as AugBDDs and published at a central location. The AugBDDs may contain history information of its construction. An ROBDD encoding dictionary may be available for all participants. A decision diagram compression value, i.e., the data size of a decision diagram based on the query result data, may be determined by considering the data size of the query result data. The above-mentioned triple set can then be concatenated to generate a bit sequence, which may be a query result bit sequence. In this regard, the discussed encoding keys, i.e., a=101, b=001, and c=011, can be stored in a dictionary. If necessary, the dictionary is transmitted along with the ROBDDs or the AugBDDs. 
     A representation for Matti&#39;s information is provided as follows:
         :Matti a :Person;   :Matti :bankaccount “50000”;   :Matti :mood :happy.       

     Where :mood is public information and :bankaccount is private information. The same information exists for other participants. The system  100  constructs ROBDDs (“BDDs”) from RDF triples/graphs representing a piece of information (e.g., :Matti a :Person.) and assigns them with IDs (e.g., BDD_ID  0 ) as follows:
         :Matti a: Person.=&gt;BDD_ID  0     :Matti :bankaccount “50000”.=&gt;BDD_ID  1     :Matti :mood :happy.=&gt;BDD_ID  2         

     The BDD_IDs are results of a hash function and thus are unique and longer than one digit. The integers (e.g., 1, 2, etc.) are used here to simplify the discussion. The system  100  creates public and private information and the associated construction history as follows:
         #Matti_secret=BDD_OR(BDD_ID  0 , BDD_ID  1 )=&gt;BDD_ID  3     #Matti_public=BDD_OR(BDD_ID  0 , BDD_ID  2 )=&gt;BDD_ID  4     #Matti_all=BDD_OR(BDD_ID  3 , BDD_ID  4 )=&gt;BDD_ID  128         

     The public and private AugBDDs are divided in a way to share the private information independently from the public information. Meanwhile, the private information BDD 1  and public information BDD 2  of Matti are combined as BDD 3  as shown in the upper portion of  FIG. 7 . In another embodiment, the system  100  traverses the history information deeper so that the #Matti_all is:
         #Matti_all=BDD_OR( 3 =BDD_OR(BDD_ID_ 0 ,BDD_ID  1 ), 4 =BDD_OR(BDD_ID_ 0 ,BDD_ID  2 ))       

     The system  100  repeats this procedure for Jenna:
         :Jenna a :Person;=&gt;BDD_ID  5     :Jenna :bankaccount “5000”;=&gt;BDD_ID  6     :Jenna :mood :inlove.=&gt;BDD_ID  7     #Jenna_secret=BDD_OR( 5 , 6 )=BDD_ID  14     #Jenna_public=BDD_OR( 5 , 7 )=BDD_ID  15     #Jenna_all=BDD_OR( 14 , 15 )=BDD_ID  16         

     The system  100  then creates a social network for some participants:
         :Matti :wife :Mervi.=&gt;BDD_ID  20     :Matti :friend :Jenna.=&gt;BDD_ID  21         

     The fact that :Matti :friend :Jenna implies :Jenna :friend :Matti. Although Jenna :friend :Matti should be assigned with its own ID, the relevant assignment and discussion are omitted to simplify the discussion. The system  100  adds the social relation information to the public information:
         #Matti_public=BDD_OR( 4 , 21 )=&gt;BDD_ID  22  # Add jenna   #Matti_public=BDD_OR( 22 , 20 )=&gt;BDD_ID  23  # Add mervi   #Jenna_public=BDD_ID  24     #Mervi_public=BDD_ID  25         

     The same procedure is repeated for other participants. The description of a protocol of agreeing to be friends in the social network is also omitted here. The system  100  goes straight to generate BDD_IDs for #Jenna_public and #Mervi_public. The procedure is the same as discussed above. The ID values are assigned in the sequence of discussion. 
     Since all of the mentioned participants belong to the same social network, the system  100  generates an aggregate BDD that corresponds to the social network in which Matti, Jenna and Mervi belong along with their public information as follows:
         #Nykanen_social=BDD_OR( 23 , 24 )=BDD_ID  26     #Nykanen_social=BDD_OR( 26 , 25 )=BDD_ID  27         

     When participating in a social network, the public information is shared and can reside anywhere (e.g., any information stores) in the smart space, and at least at the central location (e.g., a centralized information store). In another embodiment, the #Nykanen_social ( 27 ) with its history information is sent to all participants if determined as necessary. 
     A change in public information, such as the mood of Matti, triggers construction of a new AugBDD and a new hash ID, which is then communicated to at least the central location. If the resulting change produces a ROBDD graph that has been generated before, only the hash ID is sent to the central location. In the smart space, the information stores usually query for the hash IDs and the construction history, and rarely ask for a complete ROBDD graph due to its size. Now considering a change in a status of Matti, i.e., he changes his mood from :happy to :hungry, and then later back to :happy. The system  100  generates a new ID as follows:
         :Matti :mood :hungry.=&gt;BDD_ID  28 .       

     The system  100  then updates Matti&#39;s public information as follows:
         #Matti_public=BDD_OR(BDD_ID  0 ,BDD_ID  28 )=&gt;BDD_ID  29         

     When :Matti switches back to :happy :mood, the old ID becomes valid (provided no other changes have occurred) and only the old IDs are sent to the central location. The central location can use the old IDs as was before the mood change. 
     Since the original #Matti_public was included in the social graph #Nykanen_social, the system  100  can remove the original public ID from a local copy of the #Nykanen_social information and adds the new #Matti_public to it (both using BDD operations). The resulting BDD is hashed to a new BDD_ID (assuming it is truly new). The system  100  can publish that BDD_ID at the central location along with its construction history. When an information store does not recognize the hash ID (i.e., not available in the ID column of the external index table), it asks the central location or other information stores for the complete ROBDD graph. Another way of dealing with the unrecognized/unknown ID is to look at the construction history of the ROBDD (i.e., tracking a chain of construction events back and forth between the history column and the ID column of the external index table for each hash IDs involved in the construction events) and only ask for the missing elements (e.g., hash 37 , hash 12 ) in the graph and construct the ROBDD graph at the information store. The missing elements refer to hash IDs involving in construction events of other hash IDs in the table, but their own construction history is not available in the external index table. 
     In another embodiment, Jenna uses Matti&#39;s public information BDD 4  (e.g., which is combined from BDD 0 : Matti is a person or BDD 2 : Matti is happy as mentioned) to create a hash ID: BDD_ID  6  (i.e., ID_DD 6  in  FIG. 7 ) for her private information BDD 6  (i.e., Jenna&#39;s bank account balance is $5,000). As shown in the lower portion of  FIG. 7 , the construction history of BDD 6  thus includes BDD_ID 4 , BDD_ID 0  and BDD_ID  2 . When Matti&#39;s mode changes, BDD 6  changes according to data store in a database  730  containing construction history of BDD_IDs. Both public information and private information can be available at a social network but under different level of access control as shown in the column  520  of the external index table and discussed previously. 
     The system  100  can be used in a semantic web, or in a smart space architecture to be available in all locations to all nodes and entities.  FIG. 8  is a diagram of a smart space structure, according to one embodiment. Each smart space  800  includes smart space nodes/objects  833 ,  835 ,  837  and  839  and semantic information brokers (SIB)  810 ,  820  which form the nucleus of the smart space  800 . Each SIB is an entity performing triple governance in possible co-operation with other SIBs for one smart space. A SIB may be a concrete or virtual entity. Each SIB also supports the smart space nodes/objects  833 ,  835 ,  837  and  839  e.g., a user, a mobile terminal, or a PC) interacting with other SIBs through information transaction operations. The devices  831   a ,  831   b  may be any devices (e.g., a mobile terminal, a personal computer, etc.) or equipment (e.g., a server, a router, etc.). By way of example, RDF is used in the smart space  800 . The triple governance transactions in the smart space  800  uses a smart space Access Protocol (SSAP) to, e.g., join, leave, insert, remove, update, query, subscribe, unsubscribe information (e.g., in a unit of a triple). A subscription is a special query that is used to trigger reactions to persistent queries for information. Persistent queries are particular cases of plain queries. 
     The physical distribution protocol of a smart space (i.e., SSAP) allows formation of a smart space using multiple SIBs. With transactional operations, a node/object produces/inserts and consumes/queries information in the smart space  800 . As distributed SIBs belong to the same smart space  800 , query and subscription operations cover the whole information extent of a smart space. 
       FIG. 8  shows an implementation structure of the system  100  in the smart space (SS)  800 , the smart space  800  is depicted in the box in a broken line  801  (as the boundary of the smart space). There are two devices  931   a ,  931   b  connected to the smart space. In the upper part of  FIG. 8 , a dotted line  802  shows the boundaries of the devices. The devices can be mobile terminals, personal computers, servers, or the like. Each device has nodes (e.g., two) therein. Each node represents a knowledge processor (KP). KPs are entities contributing to inserting and removing contents as well as querying and subscribing content according to ontology relevant to its defined functionality. A KP needs one or more partner KPs for sharing content and for implementing an agreed semantics for the used ontology. With this implementation structure, the smart space serves private and public entities in different business domains A, B using the devices  831   a ,  831   b  and KPs running in the business domains A, in order to support the private and public entities to access information services. 
     In this embodiment, the internal and external indexing tables are embedded in the SSAP protocol at SIB_IF or ISIB_IF upon an “insert” protocol message. To build itself on top of the smart space protocol, the system  100  uses ontological constructs for the hash IDs, which is, for instance, a predefined smart_space_robdd_id concept. The SIB_IF is an interface between the SIBs and a device, and the ISIB_IF is an interface between two SIBs. 
     In one embodiment, the approach described herein is implemented at the interfaces SIB_IF and ISIB_IF of the system  100  to transmit the hash IDs. In other embodiments, one or more application programming interfaces (APIs) (e.g., third party APIs) can be used in addition to or instead of SIB_IF and ISIB_IF. The approach described herein provides performance gains while allowing multiple proprietary implementations of information stores in the smart space  800  according to  FIG. 8 . The decoding complexity for developing an application is buried below a convenience API (CONV_API) according to  FIG. 8 . Similarly, the tools for a local (at the node level) information search are provided as a part of a convenience library. However, if a malicious node produces metadata that exponentially increases a graph size (e.g., ROBDD size), the system  100  takes countermeasures such as conditional BDD encoding, and conventional node authentication methods, etc. 
     The augmentation of construction history and other information related to the ROBDD defines the ID of the data set described by the ROBDD (e.g., an ROBDD that has been embedded in the AugBDD). In one embodiment, the smart space protocol messages are checked for hash ID consistency by (1) checking for the correct (according to ontology) types of hash IDs in term of a range and a domain of the instances that have a defined property between them, and (2) checking for a correct number of hash IDs connected by the defined properties. In other words, the (1) and (2) mechanisms are applied to detect the smart_space_robdd_id concept within the smart space messages and then perform the checking for the availability of hash IDs from the external index table. The request for a missing hash ID can then be executed via a smart space query. This query relies upon the ROBDD graphs being available in a SIB in the smart space. The AugBDDs can be sent over to a remote system that uses the AugBDDs locally to check the consistency of the hash IDs or other properties in local information stores, which allows checking for ontology conformance without direct access to the ontology description. 
     The processes described herein for obtaining decision diagrams from unknown hash identifiers may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below. 
       FIG. 9  illustrates a computer system  900  upon which an embodiment of the invention may be implemented. Computer system  900  is programmed (e.g., via computer program code or instructions) to reduce communications traffic on networks by querying unknown hash identifiers as described herein and includes a communication mechanism such as a bus  910  for passing information between other internal and external components of the computer system  900 . Information (also called data) is represented as a physical expression of a measurable phenomenon, typically electric voltages, but including, in other embodiments, such phenomena as magnetic, electromagnetic, pressure, chemical, biological, molecular, atomic, sub-atomic and quantum interactions. For example, north and south magnetic fields, or a zero and non-zero electric voltage, represent two states (0, 1) of a binary digit (bit). Other phenomena can represent digits of a higher base. A superposition of multiple simultaneous quantum states before measurement represents a quantum bit (qubit). A sequence of one or more digits constitutes digital data that is used to represent a number or code for a character. In some embodiments, information called analog data is represented by a near continuum of measurable values within a particular range. Computer system  900 , or a portion thereof, constitutes a means for performing one or more steps of reducing communications traffic on networks by querying unknown hash identifiers. 
     A bus  910  includes one or more parallel conductors of information so that information is transferred quickly among devices coupled to the bus  910 . One or more processors  902  for processing information are coupled with the bus  910 . 
     A processor  902  performs a set of operations on information as specified by computer program code related to reduce communications traffic on networks by querying unknown hash identifiers. The computer program code is a set of instructions or statements providing instructions for the operation of the processor and/or the computer system to perform specified functions. The code, for example, may be written in a computer programming language that is compiled into a native instruction set of the processor. The code may also be written directly using the native instruction set (e.g., machine language). The set of operations include bringing information in from the bus  910  and placing information on the bus  910 . The set of operations also typically include comparing two or more units of information, shifting positions of units of information, and combining two or more units of information, such as by addition or multiplication or logical operations like OR, exclusive OR (XOR), and AND. Each operation of the set of operations that can be performed by the processor is represented to the processor by information called instructions, such as an operation code of one or more digits. A sequence of operations to be executed by the processor  902 , such as a sequence of operation codes, constitute processor instructions, also called computer system instructions or, simply, computer instructions. Processors may be implemented as mechanical, electrical, magnetic, optical, chemical or quantum components, among others, alone or in combination. 
     Computer system  900  also includes a memory  904  coupled to bus  910 . The memory  904 , such as a random access memory (RAM) or other dynamic storage device, stores information including processor instructions for reducing communications traffic on networks by querying unknown hash identifiers. Dynamic memory allows information stored therein to be changed by the computer system  900 . RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory  904  is also used by the processor  902  to store temporary values during execution of processor instructions. The computer system  900  also includes a read only memory (ROM)  906  or other static storage device coupled to the bus  910  for storing static information, including instructions, that is not changed by the computer system  900 . Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus  910  is a non-volatile (persistent) storage device  908 , such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system  900  is turned off or otherwise loses power. 
     Information, including instructions for reducing communications traffic on networks by querying unknown hash identifiers, is provided to the bus  910  for use by the processor from an external input device  912 , such as a keyboard containing alphanumeric keys operated by a human user, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system  900 . Other external devices coupled to bus  910 , used primarily for interacting with humans, include a display device  914 , such as a cathode ray tube (CRT) or a liquid crystal display (LCD), or plasma screen or printer for presenting text or images, and a pointing device  916 , such as a mouse or a trackball or cursor direction keys, or motion sensor, for controlling a position of a small cursor image presented on the display  914  and issuing commands associated with graphical elements presented on the display  914 . In some embodiments, for example, in embodiments in which the computer system  900  performs all functions automatically without human input, one or more of external input device  912 , display device  914  and pointing device  916  is omitted. 
     In the illustrated embodiment, special purpose hardware, such as an application specific integrated circuit (ASIC)  920 , is coupled to bus  910 . The special purpose hardware is configured to perform operations not performed by processor  902  quickly enough for special purposes. Examples of application specific ICs include graphics accelerator cards for generating images for display  914 , cryptographic boards for encrypting and decrypting messages sent over a network, speech recognition, and interfaces to special external devices, such as robotic arms and medical scanning equipment that repeatedly perform some complex sequence of operations that are more efficiently implemented in hardware. 
     Computer system  900  also includes one or more instances of a communications interface  970  coupled to bus  910 . Communication interface  970  provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general the coupling is with a network link  978  that is connected to a local network  980  to which a variety of external devices with their own processors are connected. For example, communication interface  970  may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, communications interface  970  is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a communication interface  970  is a cable modem that converts signals on bus  910  into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable. As another example, communications interface  970  may be a local area network (LAN) card to provide a data communication connection to a compatible LAN, such as Ethernet. Wireless links may also be implemented. For wireless links, the communications interface  970  sends or receives or both sends and receives electrical, acoustic or electromagnetic signals, including infrared and optical signals, that carry information streams, such as digital data. For example, in wireless handheld devices, such as mobile telephones like cell phones, the communications interface  970  includes a radio band electromagnetic transmitter and receiver called a radio transceiver. In certain embodiments, the communications interface  970  enables connection to the communication network  105  to the UE  101  for reducing communications traffic on networks by querying unknown hash identifiers. 
     The term computer-readable medium is used herein to refer to any medium that participates in providing information to processor  902 , including instructions for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as storage device  908 . Volatile media include, for example, dynamic memory  904 . Transmission media include, for example, coaxial cables, copper wire, fiber optic cables, and carrier waves that travel through space without wires or cables, such as acoustic waves and electromagnetic waves, including radio, optical and infrared waves. Signals include man-made transient variations in amplitude, frequency, phase, polarization or other physical properties transmitted through the transmission media. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape, optical mark sheets, any other physical medium with patterns of holes or other optically recognizable indicia, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. The term computer-readable storage medium is used herein to refer to any computer-readable medium except transmission media. 
     Logic encoded in one or more tangible media includes one or both of processor instructions on a computer-readable storage media and special purpose hardware, such as ASIC  920 . 
     Network link  978  typically provides information communication using transmission media through one or more networks to other devices that use or process the information. For example, network link  978  may provide a connection through local network  980  to a host computer  982  or to equipment  984  operated by an Internet Service Provider (ISP). ISP equipment  984  in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet  990 . A computer called a server host  992  connected to the Internet hosts a process that provides a service in response to information received over the Internet. For example, server host  992  hosts a process that provides information representing video data for presentation at display  914 . 
     At least some embodiments of the invention are related to the use of computer system  900  for implementing some or all of the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system  900  in response to processor  902  executing one or more sequences of one or more processor instructions contained in memory  904 . Such instructions, also called computer instructions, software and program code, may be read into memory  904  from another computer-readable medium such as storage device  908  or network link  978 . Execution of the sequences of instructions contained in memory  904  causes processor  902  to perform one or more of the method steps described herein. In alternative embodiments, hardware, such as ASIC  920 , may be used in place of or in combination with software to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware and software, unless otherwise explicitly stated herein. 
     The signals transmitted over network link  978  and other networks through communications interface  970 , carry information to and from computer system  900 . Computer system  900  can send and receive information, including program code, through the networks  980 ,  990  among others, through network link  978  and communications interface  970 . In an example using the Internet  990 , a server host  992  transmits program code for a particular application, requested by a message sent from computer  900 , through Internet  990 , ISP equipment  984 , local network  980  and communications interface  970 . The received code may be executed by processor  902  as it is received, or may be stored in memory  904  or in storage device  908  or other non-volatile storage for later execution, or both. In this manner, computer system  900  may obtain application program code in the form of signals on a carrier wave. 
     Various forms of computer readable media may be involved in carrying one or more sequence of instructions or data or both to processor  902  for execution. For example, instructions and data may initially be carried on a magnetic disk of a remote computer such as host  982 . The remote computer loads the instructions and data into its dynamic memory and sends the instructions and data over a telephone line using a modem. A modem local to the computer system  900  receives the instructions and data on a telephone line and uses an infra-red transmitter to convert the instructions and data to a signal on an infra-red carrier wave serving as the network link  978 . An infrared detector serving as communications interface  970  receives the instructions and data carried in the infrared signal and places information representing the instructions and data onto bus  910 . Bus  910  carries the information to memory  904  from which processor  902  retrieves and executes the instructions using some of the data sent with the instructions. The instructions and data received in memory  904  may optionally be stored on storage device  908 , either before or after execution by the processor  902 . 
       FIG. 10  illustrates a chip set  1000  upon which an embodiment of the invention may be implemented. Chip set  1000  is programmed to reduce communications traffic on networks by querying unknown hash identifiers as described herein and includes, for instance, the processor and memory components described with respect to  FIG. 9  incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set can be implemented in a single chip. Chip set  1000 , or a portion thereof, constitutes a means for performing one or more steps of reducing communications traffic on networks by querying unknown hash identifiers. 
     In one embodiment, the chip set  1000  includes a communication mechanism such as a bus  1001  for passing information among the components of the chip set  1000 . A processor  1003  has connectivity to the bus  1001  to execute instructions and process information stored in, for example, a memory  1005 . The processor  1003  may include one or more processing cores with each core configured to perform independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively or in addition, the processor  1003  may include one or more microprocessors configured in tandem via the bus  1001  to enable independent execution of instructions, pipelining, and multithreading. The processor  1003  may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP)  1007 , or one or more application-specific integrated circuits (ASIC)  1009 . A DSP  1007  typically is configured to process real-world signals (e.g., sound) in real time independently of the processor  1003 . Similarly, an ASIC  1009  can be configured to performed specialized functions not easily performed by a general purposed processor. Other specialized components to aid in performing the inventive functions described herein include one or more field programmable gate arrays (FPGA) (not shown), one or more controllers (not shown), or one or more other special-purpose computer chips. 
     The processor  1003  and accompanying components have connectivity to the memory  1005  via the bus  1001 . The memory  1005  includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to reduce communications traffic on networks by querying unknown hash identifiers. The memory  1005  also stores the data associated with or generated by the execution of the inventive steps. 
       FIG. 11  is a diagram of exemplary components of a mobile terminal (e.g., handset) for communications, which is capable of operating in the system of  FIG. 1 , according to one embodiment. In some embodiments, mobile terminal  1100 , or a portion thereof, constitutes a means for performing one or more steps of reducing communications traffic on networks by querying unknown hash identifiers. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the back-end encompasses all of the base-band processing circuitry. As used in this application, the term “circuitry” refers to both: (1) hardware-only implementations (such as implementations in only analog and/or digital circuitry), and (2) to combinations of circuitry and software (and/or firmware) (such as to a combination of processor(s), including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions). This definition of “circuitry” applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term “circuitry” would also cover an implementation of merely a processor (or multiple processors) and its (or their) accompanying software/or firmware. The term “circuitry” would also cover, for example, a baseband integrated circuit or applications processor integrated circuit in a mobile phone or a similar integrated circuit in a cellular network device or other network devices. 
     Pertinent internal components of the telephone include a Main Control Unit (MCU)  1103 , a Digital Signal Processor (DSP)  1105 , and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit  1107  provides a display to the user in support of various applications and mobile terminal functions that offer automatic contact matching. An audio function circuitry  1109  includes a microphone  1111  and microphone amplifier that amplifies the speech signal output from the microphone  1111 . The amplified speech signal output from the microphone  1111  is fed to a coder/decoder (CODEC)  1113 . 
     A radio section  1115  amplifies power and converts frequency in order to communicate with a base station, which is included in a mobile communication system, via antenna  1117 . The power amplifier (PA)  1119  and the transmitter/modulation circuitry are operationally responsive to the MCU  1103 , with an output from the PA  1119  coupled to the duplexer  1121  or circulator or antenna switch, as known in the art. The PA  1119  also couples to a battery interface and power control unit  1120 . 
     In use, a user of mobile terminal  1101  speaks into the microphone  1111  and his or her voice along with any detected background noise is converted into an analog voltage. The analog voltage is then converted into a digital signal through the Analog to Digital Converter (ADC)  1123 . The control unit  1103  routes the digital signal into the DSP  1105  for processing therein, such as speech encoding, channel encoding, encrypting, and interleaving. In one embodiment, the processed voice signals are encoded, by units not separately shown, using a cellular transmission protocol such as global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), satellite, and the like. 
     The encoded signals are then routed to an equalizer  1125  for compensation of any frequency-dependent impairments that occur during transmission though the air such as phase and amplitude distortion. After equalizing the bit stream, the modulator  1127  combines the signal with a RF signal generated in the RF interface  1129 . The modulator  1127  generates a sine wave by way of frequency or phase modulation. In order to prepare the signal for transmission, an up-converter  1131  combines the sine wave output from the modulator  1127  with another sine wave generated by a synthesizer  1133  to achieve the desired frequency of transmission. The signal is then sent through a PA  1119  to increase the signal to an appropriate power level. In practical systems, the PA  1119  acts as a variable gain amplifier whose gain is controlled by the DSP  1105  from information received from a network base station. The signal is then filtered within the duplexer  1121  and optionally sent to an antenna coupler  1135  to match impedances to provide maximum power transfer. Finally, the signal is transmitted via antenna  1117  to a local base station. An automatic gain control (AGC) can be supplied to control the gain of the final stages of the receiver. The signals may be forwarded from there to a remote telephone which may be another cellular telephone, other mobile phone or a land-line connected to a Public Switched Telephone Network (PSTN), or other telephony networks. 
     Voice signals transmitted to the mobile terminal  1101  are received via antenna  1117  and immediately amplified by a low noise amplifier (LNA)  1137 . A down-converter  1139  lowers the carrier frequency while the demodulator  1141  strips away the RF leaving only a digital bit stream. The signal then goes through the equalizer  1125  and is processed by the DSP  1105 . A Digital to Analog Converter (DAC)  1143  converts the signal and the resulting output is transmitted to the user through the speaker  1145 , all under control of a Main Control Unit (MCU)  1103 —which can be implemented as a Central Processing Unit (CPU) (not shown). 
     The MCU  1103  receives various signals including input signals from the keyboard  1147 . The keyboard  1147  and/or the MCU  1103  in combination with other user input components (e.g., the microphone  1111 ) comprise a user interface circuitry for managing user input. The MCU  1103  runs a user interface software to facilitate user control of at least some functions of the mobile terminal  1101  to reduce communications traffic on networks by querying unknown hash identifiers. The MCU  1103  also delivers a display command and a switch command to the display  1107  and to the speech output switching controller, respectively. Further, the MCU  1103  exchanges information with the DSP  1105  and can access an optionally incorporated SIM card  1149  and a memory  1151 . In addition, the MCU  1103  executes various control functions required of the terminal. The DSP  1105  may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP  1105  determines the background noise level of the local environment from the signals detected by microphone  1111  and sets the gain of microphone  1111  to a level selected to compensate for the natural tendency of the user of the mobile terminal  1101 . 
     The CODEC  1113  includes the ADC  1123  and DAC  1143 . The memory  1151  stores various data including call incoming tone data and is capable of storing other data including music data received via, e.g., the global Internet. The software module could reside in RAM memory, flash memory, registers, or any other form of writable storage medium known in the art. The memory device  1151  may be, but not limited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage, or any other non-volatile storage medium capable of storing digital data. 
     An optionally incorporated SIM card  1149  carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card  1149  serves primarily to identify the mobile terminal  1101  on a radio network. The card  1149  also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile terminal settings. 
     While the invention has been described in connection with a number of embodiments and implementations, the invention is not so limited but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims. Although features of the invention are expressed in certain combinations among the claims, it is contemplated that these features can be arranged in any combination and order.