Patent Publication Number: US-9430494-B2

Title: Spatial data cartridge for event processing systems

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     The present application claims the benefit and priority under 35 U.S.C. 119(e) of the following provisional applications, the entire contents of which are incorporated herein by reference for all purposes: 
     U.S. Provisional Application No. 61/290,460, filed Dec. 28, 2009, entitled EXTENSIBILITY PLATFORM USING DATA CARTRIDGES; 
     U.S. Provisional Application No. 61/311,175, filed Mar. 5, 2010, entitled EXTENSIBILITY PLATFORM USING DATA CARTRIDGES; 
     U.S. Provisional Application No. 61/327,903, filed Apr. 26, 2010, entitled EXTENSIBLE INDEXING FRAMEWORK USING DATA CARTRIDGES; and 
     U.S. Provisional Application No. 61/355,415, filed Jun. 16, 2010, entitled SPATIAL DATA CARTRIDGE FOR EVENT PROCESSING SYSTEMS. 
    
    
     BACKGROUND 
     Embodiments of the present invention relate in general to event processing, and in particular to a spatial data cartridge for event processing systems. 
     Databases have traditionally been used in applications that require storage of data and querying capability on the stored data. Existing databases are thus best equipped to run queries over a finite stored data set. The traditional database model is however not well suited for a growing number of modern applications in which data is received as a stream of data events instead of being stored as a bounded data set. A data stream, also referred to as an event stream, is characterized by a real-time, potentially continuous, sequence of events. A data or event stream thus represents a potentially unbounded stream of data. Examples of sources of events can include various sensors and probes (e.g., RFID sensors, temperature sensors, etc.) configured to send a sequence of sensor readings, financial tickers sending out pricing information, network monitoring and traffic management applications sending network status updates, events from click stream analysis tools, global positioning systems (GPSs) sending GPS data, and others. 
     Oracle Corporation™ provides a system (referred to as a Complex Event Processing, or CEP, system) for processing such event streams. A CEP system is quite different from a relational database management system (RDBMS) in which data is stored in a database and then processed using one or more queries. In a CEP system, a query is run continuously and query processing is performed in real-time as events in a stream are received by the system. 
     A CEP system can receive data events from various different sources for various different applications. Accordingly, the data that is received may not follow a fixed format or schema but may be more heterogeneous in nature (e.g., binary data, XML data without an associated schema, etc.). For example, the data that is received may include streams of image data for an image processing application, streams of audio data for an audio processing application, streams of spatial or geographic or location data for a GPS application, streams of stock data for a financial application, and the like. As a result of the different data types and sources and their different data manipulation requirements, specialized functions or methods are usually needed to process the streaming data. While a CEP system can provide support for some native data types and/or methods/functions for the native data types, these native data types or functions are many times not sufficient to cover the diverse types of processing needed by applications that use a CEP system. 
     As a result, processing platforms, such as CEP systems, constantly have to be extended by application developers and service providers to support heterogeneous data formats and their data manipulation mechanisms in order to interact/interoperate with diverse sources of events and data. For example, consider a CEP system that processes localization events emitted by GPS devices. Such a CEP system would need to understand spatial data formats and functions related to the spatial data format. 
     In the past, the capabilities of a CEP system were extended exclusively through user defined functions (UDFs) or special code (e.g., customized Java beans). To achieve extensibility, an application developer for a specific application had to define customized user defined functions (UDFs) to interact with the specialized application. The application developer had to design one function at a time and define the function&#39;s interface based upon predefined data types provided by the CEP system. 
     However, this process has several drawbacks and inefficiencies. The UDFs that are designed are application-scoped and thus are hard to re-use amongst other applications of the CEP system. The UDFs cannot be reused since they are closely coupled or tied to the application defining the UDF. For example, a UDF defined for a video-processing application cannot be used in another application. Further, the UDFs are individually defined and cannot be grouped into domains (e.g., spatial), therefore making their management difficult. Additionally, UDFs provide a poor programming experience, as the usage of the extension in the form of a UDF is not transparent to the user. 
     BRIEF SUMMARY 
     Embodiments of the present invention provide techniques for extending the capabilities of an event processing system to support the processing of spatial data. In one set of embodiments, this extensibility can be provided via a plug-in extension component referred to herein as a spatial data cartridge. The spatial data cartridge can enable the event processing system to support spatial data types (e.g., point, polygon, etc.) and various operations related to such data types (e.g., proximity determinations, overlap determinations, etc.). The spatial data cartridge can also define an indexing scheme that can be integrated with the capabilities of the event processing system to support the indexing of spatial data. Using the spatial data cartridge, the event processing system can operate on spatial data even if spatial data formats are not natively supported by the system. 
     According to one embodiment of the present invention, a system is provided that includes a storage component configured to store a spatial data cartridge including metadata pertaining to a spatial function and code that implements the spatial function, where the spatial function is configured to determine a topological relationship between first spatial data and second spatial data. The system further includes a processor configured to receive a query referencing the spatial function, the query being adapted to process one or more data streams, compile the query based on the metadata included in spatial data cartridge, and execute the query based on the code included in the spatial data cartridge. 
     In one embodiment, the system is an event processing system. 
     In one embodiment, the query is a Continuous Query Language (CQL) query. 
     In one embodiment, the query includes a link definition that specifies the spatial function and the spatial data cartridge, and the processor is configured to identify the spatial data cartridge based on the link definition. 
     In one embodiment, the spatial function is selected from a group consisting of: contains, inside, withindistance, overlaps, touch, covers, and coveredby. 
     In one embodiment, the spatial data cartridge further includes metadata pertaining to one or more spatial data types and one or more spatial indexes. 
     In one embodiment, the one or more spatial data types are selected from a group consisting of: point, curve, polygon, and solid. 
     In one embodiment, compiling the query includes retrieving the metadata pertaining to the spatial function from the spatial data cartridge, performing semantic analysis of the query based on the metadata, and generating instructions for executing the query. 
     In one embodiment, executing the query includes executing the code that implements the spatial function in the spatial data cartridge. 
     In one embodiment, executing the code that implements the spatial function includes performing a first filter operation with respect to data in a first spatial data stream and data in a second spatial data stream, the first filter operation returning a superset of an exact result set for the spatial function; and performing a second filter operation with respect to the superset, the second filter operation returning the exact result set for the spatial function. 
     In one embodiment, performing the first filter operation includes comparing geometric approximations of the data in the first spatial data stream and the data in the second spatial data stream; and determining likely topological relationships between the data in the first spatial data stream and the data in the second spatial data stream based on the comparing. 
     In one embodiment, performing the second filter operation includes comparing exact geometries of the data in the first spatial data stream and the data in the second spatial data stream; and determining exact topological relationships between the data in the first spatial data stream and the data in the second spatial data stream based on the comparing. 
     In one embodiment, performing the first filter operation includes performing an index scan of a spatial index created for the first spatial data stream or the second spatial data stream. 
     In one embodiment, the storage component is further configured to store spatial context information, and executing the query is further based on the spatial context information. 
     In one embodiment, the spatial context information identifies a geometric coordinate system. 
     According to another embodiment of the present invention, a non-transitory computer-readable storage medium is provided that has stored thereon instructions executable by a processor. The instructions include instructions that cause the processor to register a spatial data cartridge including metadata pertaining to a spatial function and code that implements the spatial function, the spatial function being configured to determine a topological relationship between first spatial data and second spatial data. The instructions further include instructions that cause the processor to receive a query referencing the spatial function, the query being adapted to process one or more data streams, instructions that cause the processor to compile the query based on the metadata included in spatial data cartridge; and instructions that cause the processor to execute the query based on the code included in the spatial data cartridge. 
     According to another embodiment of the present invention, a method is provided that includes registering, by a computer system, a spatial data cartridge including metadata pertaining to a spatial function and code that implements the spatial function, the spatial function being configured to determine a topological relationship between first spatial data and second spatial data. The method further includes receiving, by the computer system, a query referencing the spatial function, the query being adapted to process one or more data streams, compiling, by the computer system, the query based on the metadata included in spatial data cartridge, and executing, by the computer system, the query based on the code included in the spatial data cartridge. 
     The foregoing, together with other features and embodiments will become more apparent when referring to the following specification, claims, and accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a simplified block diagram of an event processing system in accordance with an embodiment of the present invention. 
         FIG. 2  is a simplified block diagram of a data cartridge in accordance with an embodiment of the present invention. 
         FIG. 3  is another simplified block diagram of an event processing system in accordance with an embodiment of the present invention. 
         FIG. 4  is a flow diagram of a process performed by an event processing system for compiling a query using a data cartridge in accordance with an embodiment of the present invention. 
         FIG. 5  is a flow diagram of a process performed by an event processing system for executing a query using a data cartridge in accordance with an embodiment of the present invention. 
         FIG. 6  is a flow diagram of a process performed by a spatial data cartridge for executing a spatial function in a query in accordance with an embodiment of the present invention. 
         FIG. 7  is a simplified block diagram illustrating components of a system environment that can be used in accordance with an embodiment of the present invention. 
         FIG. 8  is a simplified block diagram of a computer system that can be used in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the invention. However, it will be apparent that certain embodiments can be practiced without these specific details. 
     Embodiments of the present invention provide techniques for extending the capabilities of an event processing system to support the processing of spatial data. In one set of embodiments, this extensibility can be provided via a plug-in extension component (i.e., a “data cartridge”) referred to as a spatial data cartridge. The spatial data cartridge can enable the event processing system to support spatial data types (e.g., point, polygon, etc.) and various operations related to such data types (e.g., proximity determinations, overlap determinations, etc.). The spatial data cartridge can also define an indexing scheme that can be integrated with the capabilities of the event processing system to support the indexing of spatial data. 
     As used herein, a “data cartridge” is a self-contained manageable unit that provides information for extending the capabilities of an event processing system. Examples of capabilities that can be extended include providing support for extensible data types, functions, indexing options, different sources of data, and others. A spatial data cartridge is a particular type of data cartridge that extends the capabilities of the event processing system to support the processing and indexing of spatial data (e.g., geographic or location data). 
     In one set of embodiments, the spatial data cartridge described herein can be used in conjunction with an event processing system such as the Complex Event Processing (CEP) system/server provided by Oracle Corporation™. 
       FIG. 1  is a simplified block diagram of a system  100  according to an embodiment of the present invention. As shown, system  100  includes an event processing system  102  that is configured to process event streams. Event processing system  102  can be a CEP system such as the one provided by Oracle Corporation™. Other event processing systems provided by other vendors can be used in alternative embodiments. The embodiment depicted in  FIG. 1  is not intended to limit the scope of embodiments of the invention. Variations having more or less components than shown in  FIG. 1  are possible in alternative embodiments. 
     Event processing system  102  can receive one or more inputs  104 . Inputs  104  can include one or more event streams received from one or more sources. For example, as depicted in  FIG. 1 , event processing system  102  can receive an event stream  106  from a source S 1   108 , an event stream  110  from a source S 2   112 , and another event stream  114  from a source S 3   116 . The sources can be diverse; for example, source S 1  can be an RFID sensor providing a stream of sensor readings, source S 2  can be a GPS device providing a stream of spatial coordinates, and source S 3  can be a financial server providing a stream of stock prices. Accordingly, the type of events received on one stream can be different from events received on another stream. Event processing system  102  can receive the streams via a push-based mechanism, a pull-based mechanism, or other types of mechanisms. 
     In one set of embodiments, an event stream can be a real-time sequence of events. In a particular embodiment, an event stream can correspond to a sequence of &lt;tuple, timestamp&gt; pairs, with the tuples representing the data portion of the stream. The timestamps associated with the tuples can define a chronological order over the tuples in the stream. In one set of embodiments, the timestamps can be set by an application (e.g., within event processing system  102 ) configured to receive and/or process the event stream. For example, the receiving application can timestamp each tuple/event upon receipt In other embodiments, the timestamps can be set an application configured to send out the event stream. In certain embodiments, multiple tuples can be associated with the same timestamp in a stream. For purposes of the present disclosure, the terms “tuple” and “event” are used interchangeably. 
     Inputs  104  can also include other inputs  118  such collections of elements (e.g., a relation). These other inputs  118  can be received from various sources including applications executing on external systems or even on event processing system  102 . For example, other inputs  118  can comprise datasets (e.g., relations) configured by applications executing on systems external to event processing system  102  or on event processing system  102 . In certain embodiments, the contents of a relation can vary over time. For example, the contents of a relation can change over time by adding one or more elements to the relation, deleting one or more elements from the relation, or updating the relation. 
     In various embodiments, event processing system  102  can process received inputs  104  and generate one or more outbound event streams as a result of the processing. The processing of inputs  104  can be based upon rules configured for event processing system  102  that determine the runtime behavior of the system. In a particular embodiment, these rules can be expressed as queries using a query language. An example of such a query language is Continuous Query Language (referred to herein as CQL). Generally speaking, CQL is a query language that is based upon SQL, with added constructs that support streaming data. A query written using CQL can be referred to as a CQL query. The queries can be used for processing inputs  104  and generating outbound event streams. Queries typically perform filtering and aggregation functions to discover and extract one or more events from the input streams. The CQL queries thus determine the runtime behavior of event processing system  102 . The queries can represent the runtime conditions that are to be monitored over the streams. 
     The queries executed by an event processing system, such as event processing system  102  depicted in  FIG. 1 , are different from queries that are executed in a typical relational database management system (RDBMS). In an RDBMS, the data is stored in a database and a query is executed over the stored data. The lifetime of the query thus ends upon its execution. In event processing system  102 , due to the streaming nature of the inputs, queries are run over a continuing period of time over time-varying data received over inputs such as input streams. Accordingly, these queries are referred to as continuous queries. 
     The outbound streams generated by event processing system  102  from the processing of the input streams can be provided to one or more applications. For example, as depicted in  FIG. 1 , an outbound stream  120  can be provided to application A 1   122 , a second outbound stream  124  can be provided to application A 2   126 , and a third outbound stream  128  can be provided to application A 3   130 . In certain embodiments, an application receiving an outbound stream can perform further processing on the stream. The applications receiving the outbound stream can be executing on event processing system  102  or some other system. 
     In one set of embodiments, event processing system  102  can natively support a fixed set of data types and operations on those data types (referred to herein as native data types and operations). For purposes of the present disclosure, the terms operation and function are used synonymously. In some situations, these native data types and operations may not sufficient to support the heterogeneous data formats received via the input streams  104  and the functions (e.g., data manipulation functions) related to the data formats. Thus, in certain embodiments, the capabilities of event processing system  102  can be extended through the use of one or more data cartridges  132 . Generally speaking, data cartridges  132  enable event processing system  102  to support data types, operations, indexing schemes, and other objects not natively supported by the system. For example, in a particular embodiment, data cartridges  132  can include a spatial data cartridge that enables event processing system  102  to process and index spatial data (e.g., geographic or location data). The notion of a spatial data cartridge is discussed in greater detail below. 
     With a framework supporting data cartridges, CQL queries specified for event processing system  102  can not only reference capabilities provided natively by event processing system  102 , but can also reference extended capabilities provided by one or more data cartridges  132 . For example, a CQL query can refer to extensible objects (e.g., data types, functions, indexes, sources) defined by, e.g., a spatial data cartridge supporting spatial objects, a java data cartridge supporting java objects, and so on. In certain embodiments, references to data types and operations that are not supported natively by event processing system  102  can be seamlessly integrated with native data types and operations in the same query. In this manner, data cartridges  132  enable event processing system  102  to be easily extended beyond its native capabilities. As discussed in greater detail below. the processing to support such queries can be automatically taken care of by interactions between event processing system  102  and data cartridges  132 . 
       FIG. 2  is a simplified block diagram of a data cartridge  132  according to an embodiment of the present invention. As shown, data cartridge  132  can store information for one or more extensible objects  200 . Examples of such extensible objects include data types, functions, indexes, sources, and others. In one set of embodiments, the information stored for each extensible object  200  can include at least two components or portions: (1) a compile-time (or metadata) component  202  that describes the extensible object in sufficient detail so that the object can be compiled; and (2) a runtime component  204  that can be invoked at execution time or runtime. 
     In one set of embodiments, compile-time component  202  can be used for compilation of queries (e.g., CQL queries). The compile-time component of an extensible object can include information (referred to generically as metadata) that describes the extensible object in enough detail so that the compilation of queries referencing the extensible object can perform all the necessary syntactic and semantic analyses and generate execution instructions that are executable at runtime. In some embodiments, extensible objects  200  can be of different types or classes. In these cases, each different object type/class can define a different set of metadata. In one embodiment, the metadata for a particular extensible object can include the signature of the extensible object&#39;s methods, fields, and constructors. 
     In various embodiments, all of the metadata provided by data cartridge  132  can be managed by the cartridge itself and not by event processing system  102 . This avoids the need to keep data in-sync between data cartridge  132  and event processing system  102  or to pollute event processing system  102  with external data definitions. More details on how these components or portions of a data cartridge are used are provided below. 
     Referring back to  FIG. 1 , event processing system  102  can further include a compiler  134  and a runtime engine  136 . Compiler  134  can be configured to compile one or more queries  138  (e.g., CQL queries) and generate executable code/instructions  140 . In one set of embodiments, compiler  132  can use the compile-time components stored for the various extensible objects in a data cartridge to facilitate the compilation process. Code/instructions  140  generated as a result of the compilation can be executed during runtime to process incoming events. In certain embodiments, code/instructions  140  can comprise call-outs to functions that are implemented by runtime component  204  stored in data cartridge  132  for each extensible object. In this manner, a data cartridge provides both compile-time support and runtime implementations for an extensible object. The outbound data streams generated by the execution of code/instructions  140  can then be forwarded to one or more applications (e.g.,  122 ,  126 ,  130 ). 
     In the embodiment of  FIG. 1 , compiler  134  and runtime engine  136  are shown as being part of the same event processing system  102 . In alternative embodiments, these components can be resident on different systems. For example, in a particular embodiment, compiler  132  can be resident on a first system and runtime engine  136  can be resident on a second system, where both systems have access to the requisite data cartridges. 
     Several interactions can take place between event processing system  102  and a data cartridge  132  during query compilation and query runtime execution. For example, during the compilation phase, compiler  134  can receive from, and send to, data cartridge  132  information that facilitates compilation of the query and generation of executable code. During the runtime execution phase, execution of the code generated during the compilation phase can cause interactions and exchange of information between runtime engine  136  and data cartridge  132 . For example, whenever a callout is encountered in the executable code and the implementation of the callout is provided by data cartridge  132 , event processing system  102  can interact with the data cartridge. 
       FIG. 3  is another simplified block diagram of event processing system  102  according to an embodiment of the present invention. As in  FIG. 1 , event processing system  102  includes a compiler  134  and a runtime engine  136 . Further, as shown in  FIG. 3 , compiler  134  can include a lexer/parser  302 , a semantic analyzer  304 , a locator module  306 , and a code generator module  310 . The components of compiler  134  can be implemented in software (code or instructions executed by a processor) or hardware, or combinations thereof. The software can be stored on a non-transitory computer-readable storage medium. The embodiment of event processing system  102  depicted in  FIG. 3  is not intended to limit the scope of embodiments of the invention. Variations having more or less components than shown in  FIG. 3  are possible in alternative embodiments. 
     At a conceptual level, the processing performed by event processing system  102  can be divided into design-time (or compile-time) processing and runtime processing. During design-time processing, compiler  134  can receive one or more continuous queries configured for the event processing system and can compile the queries. This compilation can result in the generation of executable code/instructions  140 . One or more CQL queries can be compiled as a set to generate executable code/instructions  140 . During runtime processing, runtime engine  136  can execute code/instructions  140  to process the incoming event streams  104 . 
     Accordingly, at design-time, one or more queries (e.g., CQL queries)  138  can be provided as inputs to compiler  134 . Parser  302  of compiler  134  can parse the queries based upon a grammar. For example, a CQL query can be parsed according to a CQL grammar. The tokens generated by parser  302  from parsing the query can then be passed to semantic analyzer  304  for further processing. 
     In one set of embodiments, the association between an extensible object and a repository (e.g., a data cartridge) storing metadata for the object is done though a link name or definition, which is specified in the query using the query language. In a particular embodiment, a CQL query programmer can use the following CQL code syntax to define a link definition in a query: 
     object@source 
     In this embodiment, the @ symbol signals to the compiler that a link definition is present. The string immediately before the @ symbol refers to an object or component (e.g., an extensible object) that is to be compiled and the string immediately after the @ symbol identifies the source or repository of the metadata to be used for compiling the object. The two strings are tokenized by parser  302  and provided to semantic analyzer  304  for semantic analysis. In this manner, a link definition can be provided at the query language level that enables compiler  134  of event processing system  102  to identify the component to be compiled and the source of the metadata (e.g., a data cartridge) to be used for compiling that query component. In one embodiment, a default data cartridge can be used if no specific data cartridge is identified. 
     Usage examples include: 
     (1) foo@java 
     where “foo” identifies an object or component (e.g., an extensible function) that is to be compiled using a “java” data cartridge. The “java” data cartridge stores metadata to be used for compiling the identified “foo” object. The “foo” object can be an extensible object such as an extensible data type, an extensible index, etc.
 
(2) foo@scala
 
Here, the component “foo” is to be compiled using a data cartridge named “scala” (different from the “java” data cartridge) that provides the metadata to be used for compiling the “foo” object. Note that the “foo” object is this example is not the same object as in the previous example; they are different objects since they are owned by different cartridges.
 
(3) CONTAINS@SPATIAL(R1.polygon, R2.point)
 
Here, “CONTAINS” identifies an extensible function defined within the “SPATIAL” data cartridge. As part of the parsing performed by parser  302 , the arguments (if any) defined for a function can be determined and tokenized. In this example, the arguments of function CONTAINS include “R1.polygon” and “R2.point.”
 
     In one set of embodiments, before a data cartridge can be used by an event processing system, the data cartridge has to be registered with the event processing system. Various data cartridges can be registered with event processing system  102 . The registration information stored for a data cartridge can identify the name of the data cartridge, e.g., “scala,” “java,” etc. This registration information can be stored in a registry of event processing system  102  and used during the compilation phase. For example, when a particular data cartridge is identified by a link definition in a query, information for the data cartridge can be fetched from the registration repository. 
     As described above, as part of the compilation process, parser  302  of event processing system  102  can parse CQL query  138  to identify occurrences of link definitions in the query. In one embodiment, the processing can include parsing the CQL query to look for occurrences of the @ symbol, and for each occurrence, determining the object to be compiled, the source of metadata for compiling the object, and arguments, if any, to the object. The tokens generated by parser  302  can then be passed to semantic analyzer  304  for semantic analysis. 
     Semantic analyzer  304  can perform semantic analysis on the query, such as type checking. In certain embodiments, for a set of tokens received from parser  302 , semantic analyzer  304  can invoke a locator  306  to retrieve metadata to be used for performing semantic analysis related to the tokens. For example, based upon the tokens received from parser  302 , semantic analyzer  304  can send a request to locator  306  to locate the metadata source or repository (e.g., a data cartridge) identified by a token. In response, locator  306  can provide semantic analyzer  304  a handle to the requested metadata source or repository. 
     In one set of embodiments, the repository can be a system that is internal to event processing system  102 . For example, for natively supported data types and/or operations, the metadata can be provided by a built-in manager  308  of event processing system  102 . For extensible objects that are not natively supported by event processing system  102 , the repository can be a data cartridge  132  that is registered with event processing system  102 . 
     Semantic analyzer  304  can then access or retrieve the requisite metadata stored by the metadata source using the handle provided by locator  306 . Semantic analyzer  304  can use this retrieved information to perform semantic analysis. In one set of embodiments, using the handle, semantic analyzer  304  can interact with the metadata source via well-known interfaces provided by the developer of the repository. For example, if the metadata source is data cartridge  132 , the data cartridge can provide well-known interfaces created by the data cartridge developer to enable semantic analyzer  304  to interact with the data cartridge. These well-known interfaces can be developed by the data cartridge developer according to predetermined interface standards that allow data cartridge  132  to be compatible with a data cartridge infrastructure provided by event processing system  102 . 
     From the perspective of semantic analyzer  304 , it does not matter whether the handle returned by locator  306  is a handle to a data cartridge or some other source; both handles are treated and interacted with in a similar manner. Locator  306  thus provides the interface between compiler  134  and the source of the metadata that enables the source of the metadata to be decoupled from compiler  134 . This enables the metadata to be provided from any source, including a source within event processing system  102  or a data cartridge  132 . Additionally, the source of the metadata can be distributed, for example, made available in a cloud, etc. 
     For instance, in examples (1), (2), and (3) shown above, semantic analyzer  304  can request locator  306  to get handles to data cartridges “java,” “scala,” and “SPATIAL.” These data cartridges can be pre-registered with event processing system  102  and information related to the registered data cartridges, including the names of the data cartridges and handles to the data cartridges, can be stored in a registry. Locator  306  can perform a lookup in this registry to get a handle to the requested data cartridge and provide the handle to semantic analyzer  304 . 
     Upon receiving a handle to a metadata source such as data cartridge  132 , semantic analyzer  304  can interact with the data cartridge using published interfaces. For example, semantic analyzer  304  can use the interfaces to retrieve metadata from the data cartridge and use the retrieved metadata to perform semantic analysis of the query, including performing type checking for extensible objects included in the query. The result of the semantic analysis performed by semantic analyzer  304  is an intermediate representation that can be provided to code generator  310  for further analysis/processing. 
     In one set of embodiments, for a particular extensible object, the metadata provided to semantic analyzer  304  by data cartridge  132  for compilation of the extensible object can include information identifying one or more factories to be used for creating one or more instances of the extensible object. The metadata provided to compiler  134  can also include application context information that is used during runtime processing. For example, when performing spatial data analysis, a specific coordinate system usually need to be specified for performing the analysis. Different spatial data applications can use different coordinate systems. The application context information can be used to specify the coordinate system to be used during runtime for an application. This context information can be provided by data cartridge  132  to semantic analyzer  304  (or in general to compiler  134 ). In this manner, data cartridge  132  can provide information to event processing system  102  during the compilation phase that is to be used during the runtime phase. In certain embodiments, this application context information can be configured by a developer of the data cartridge. Thus, the data cartridge developer can set parameters to be used for runtime processing. 
     Code generator  310  can generate an execution plan for the query being compiled and can generate execution structures (e.g., executable code/instructions  140 ) based upon the execution plan. The execution structures that are generated can include instances of extensible objects referenced in the query. The extensible object instances can be created using one or more factories identified in the metadata retrieved from the data cartridge during compilation. 
     Executable instructions  140  generated by compiler  134  can then be executed at runtime by runtime engine  136  with respect to events received via an input stream  104 . The instructions can comprise one or more call-out instructions whose implementations are provided by the runtime component stored by the data cartridge for the extensible object. A call-out instruction executes an invokable component that is part of the runtime component stored by the data cartridge for the extensible object. In one embodiment, a call-out invokes an “execute” call-back (i.e., function), whose implementation is provided by the data cartridge. This “function” in the data cartridge can be implemented using different programming languages, such as a Java type, a Hadoop function, a Scala class, etc. The call-out instruction thus provides a handoff between runtime engine  136  and data cartridge  132 . 
     In the examples discussed above, the queries are expressed in CQL. Accordingly, compiler  134  and runtime engine  136  can be together referred to as the CQL engine of event processing system  102 . In alternative embodiments, other languages that provide features for stream-based processing can also be used for configuring queries executed by event processing system  102 . 
     The use of data cartridges thus enables event processing system  102  to handle complex data types and related functions that are not natively supported by the system. For example, object-oriented data types that cannot be natively supported by an event processing system can be supported by the event processing system via data cartridges. In a particular embodiment, use of a Java data cartridge can enable event processing system  102  to support object-oriented data types (classes) and programming constructs. 
       FIG. 4  is a flow diagram of a process  400  for compiling a query in an event processing system using a data cartridge according to an embodiment of the present invention. In various embodiments, process  400  can be performed by software (e.g., program, code, instructions) executed by a processor, hardware, or combinations thereof. The software can be stored on a non-transitory computer-readable storage medium. In a particular embodiment, process  400  can be performed by compiler  134  of  FIGS. 1 and 3 . 
     As shown, processing can be initiated upon receiving a query to be compiled (block  402 ). In some embodiments, multiple queries can be received and compiled together as a set. However, for the sake of simplicity, it is assumed that one query is received in process  400 . The query received in  402  can be, for example, a CQL query. The query can be received from various sources, such as sources  108 ,  112 ,  116 ,  118  of  FIG. 1 . 
     At block  404 , the query can be parsed by a compiler  134  into a set of tokens. As part of this step, compiler  134  can identify (via, e.g., parser  302 ) one or more link definitions included in the query. These link definitions can identify extensible objects used in the query, as well as their corresponding data cartridges. Compiler  132  can then determine the data cartridges needed for compiling the query based upon the link definitions (block  406 ). 
     At block  408 , compiler  134  can retrieve metadata from the data cartridge(s) determined at block  406 . In one set of embodiments, compiler  134  first obtains (via, e.g., locator  306 ) a handle to each data cartridge. Using the handle, compiler can access metadata from the data cartridge via one or more well-known interfaces provided by the data cartridge. 
     At block  410 , compiler can perform (via, e.g., semantic analyzer  304 ) various types of semantic analysis on the parsed query using the metadata retrieved at block  408 . Such analysis can include, for example, type checking. 
     An execution plan can then determined for the query, and code/instructions can be generated based upon the execution plan (blocks  412 ,  414 ). In one set of embodiments, the processing performed at block  414  can include instantiating execution structures for the query. The code/instructions generated at block  414  (including the execution structures) can be stored on a non-transitory computer-readable storage medium. In a particular embodiment, the execution structures can be generated using one or more factories identified by the metadata retrieved at block  408 . The code/instructions can then be executed during runtime for processing event streams received by event processing system  102 . 
     Although not shown in  FIG. 4 , in certain embodiments the metadata retrieved from a data cartridge at block  408  can include application context information that is to be used during runtime processing. This application context information can be incorporated into the code/instructions generated in  414 . 
     It should be appreciated that process  400  is illustrative and that variations and modifications are possible. Steps described as sequential can be executed in parallel, order of steps can be varied, and steps can be modified, combined, added, or omitted. One of ordinary skill in the art will recognize many variations, modifications, and alternatives. 
       FIG. 5  is a flow diagram of a process  500  performed by an event processing system for executing a query using a data cartridge according to an embodiment of the present invention. In certain embodiments, process  500  can correspond to the runtime processing performed by event processing system  102  for executing the query compiled in  FIG. 4 . In a particular embodiment, process  500  can be performed by runtime engine  136  of system  102 . 
     At block  502 , runtime engine  136  can receive an input event  500  via an input stream (e.g.,  108 ,  112 ,  116 ,  118 ) received by event processing system  102 . Runtime engine  136  can then process input event  500  with respect to a query by executing the code/instructions generated for the query at block  414  of  FIG. 4 . 
     During execution of the code/instructions, runtime engine  136  can encounter a call-out function whose implementation (e.g.,  508 ) is provided by data cartridge (e.g.,  132 ). In response, runtime engine  136  can invoke the call-out function, which causes implementation  508  within data cartridge  132  to be executed (block  506 ). Implementation  508  can process input event  508 , and can return the results of the processing to runtime engine  136 . An output event  512  can then be generated based upon the processing (block  510 ). In various embodiments, the output event can be provided to one or more applications via an outbound stream (e.g.,  102 ,  124 ,  128  of  FIG. 1 ). 
     Although not shown in  FIG. 5 , in certain embodiments runtime engine  136  can pass application context information to data cartridge  132  when invoking the call-out function at block  506 . This application context information can correspond to the context information received from the data cartridge during the compilation process of  FIG. 4 . Data cartridge can then execute the function/operation based on the application context information. For example, if data cartridge  132  is configured to provide support for spatial data types, and if the function invoked at block  506  is a spatial function (e.g., CONTAINS), runtime engine  136  may pass application context information including a spatial coordinate system to data cartridge  132 . Data cartridge  132  can then execute the CONTAINS function on input event  500  with respect to the coordinate system specified in the application context information. 
     It should be appreciated that process  500  is illustrative and that variations and modifications are possible. Steps described as sequential can be executed in parallel, order of steps can be varied, and steps can be modified, combined, added, or omitted. One of ordinary skill in the art will recognize many variations, modifications, and alternatives. 
     As discussed above, embodiments of the present invention provide an infrastructure for extending the native capabilities of an event processing system via data cartridges. In one set of embodiments, the event processing system can interact with a data cartridge at query compilation time to retrieve metadata regarding extensible objects and to generate executable code/instructions for the query. Since the metadata for extensible objects is entirely contained within the data cartridge, the event processing system does not need to store any information pertaining to the objects. 
     In further set of embodiments, the event processing system can interact with the data cartridge at runtime to facilitate execution of the query. For example, when a call-out to a data cartridge function is encountered during execution of the query, the system can hand over processing to the data cartridge, which can execute the function as implemented within the cartridge. In various embodiments, the call-out can refer to a function related to an extensible object provided by the data cartridge. Since the implementation of the function is entirely contained within the data cartridge, the event processing system does not need to maintain any implementations or other code for extensible objects. 
     Thus, the data cartridge framework of the present invention provides a modular and flexible mechanism for extending the native capabilities of an event processing system. In particular, each data cartridge can be a self-contained, modular unit configured to store all of the compile-time metadata and runtime code need to support extensible objects. These data cartridges can then be dynamically registered/enabled on a particular system on an as needed basis to provide the additional features supported by the data cartridges. Since all data pertaining to extensible objects is maintained by the data cartridge, the event processing system does not need to be modified or customized for each desired feature. 
     This is substantially different from the use of UDFs (described in the Background section), where UDF-related needs to be stored in the memory of the event processing system. A data cartridge thus increases the scalability and usability of an event processing system. For example, data cartridges can be added to an event processing system without having to increase the memory and/or storage resources of the system. 
     In addition, the data cartridge framework of the present invention is preferable over UDFs because data cartridges can be reused by different applications, and even by different event processing systems. Further, data cartridges can assemble or group into several domain-specific extensible objects into a single manageable unit. Data cartridges can also provide a deeper integration with the native query language of an event processing system (e.g., CQL), thus providing a simpler programming experience. 
     In certain embodiments, the data cartridge framework enables the rapid integration of an event processing system with other technologies, such as the Java language, spatial manipulation services, Oracle RDBMS, data mining, and the like that cannot be otherwise supported by the event processing system. In one set of embodiments, a particular type of data cartridge (referred to herein as a spatial data cartridge) can be provided that can extend the capabilities of an event processing system to specifically support the processing of spatial data (e.g., geographic, geometric, or location data). For example, a spatial data cartridge can enable an event processing system to compile and execute CQL queries that reference spatial data streams and spatial operations over those streams. The spatial data cartridge can also enable the event processing system to efficiently index spatial data. Support for spatial data processing can be useful in a wide variety of applications, such as automobile traffic monitoring, emergency services, air traffic control, and the like, where it can be necessary to process continuous streams of two-dimensional or three-dimensional location data (e.g., geographic coordinates, etc.). 
     The following table identifies spatial data types that can be supported by the spatial data cartridge according to an embodiment of the present invention. As used herein, a “spatial data type” is a data type is that configured to describe spatial (e.g., geographic, geometric, location-based, etc.) data. Other spatial data types not listed below can also be supported. 
     
       
         
           
               
               
             
               
                   
               
               
                 Spatial Data Type 
                 Description 
               
               
                   
               
             
            
               
                 POINT 
                 Geometry contains one point. 
               
               
                 CURVE 
                 Geometry contains one line string that can contain straight or 
               
               
                   
                 circular arc segments, or both. (LINE and CURVE are synonymous 
               
               
                   
                 in this context). 
               
               
                 POLYGON, SURFACE 
                 Geometry contains one polygon with or without holes or one surface 
               
               
                   
                 consisting of one or more polygons. In a three-dimensional polygon, 
               
               
                   
                 all points must lie on the same plane. 
               
               
                 COLLECTION 
                 Geometry is a heterogeneous collection of elements. 
               
               
                   
                 COLLECTION is a superset that includes all other types. 
               
               
                 MULTIPOINT 
                 Geometry has one or more points (MULTIPOINT is a superset of 
               
               
                   
                 POINT). 
               
               
                 MULTICURVE 
                 Geometry has one or more line strings. (MULTICURVE and 
               
               
                   
                 MULTILINE are synonymous in this context, and each is a superset 
               
               
                   
                 of both LINE and CURVE). 
               
               
                 MULTIPOLYGON, 
                 Geometry can have multiple, disjoint polygons (more than one 
               
               
                 MULTISURFACE 
                 exterior boundary) or surfaces (MULTIPOLYGON is a superset of 
               
               
                   
                 POLYGON, and MULTISURFACE is a superset of SURFACE). 
               
               
                 SOLID 
                 Geometry consists of multiple surfaces and is completely enclosed 
               
               
                   
                 in a three-dimensional space. Can be a cuboid or a frustum. 
               
               
                 MULTISOLID 
                 Geometry can have multiple, disjoint solids (more than one exterior 
               
               
                   
                 boundary). (MULTISOLID is a superset of SOLID). 
               
               
                   
               
            
           
         
       
     
     The following table identifies spatial operators/functions that can be supported by the spatial data cartridge according to an embodiment of the present invention. As used herein, a “spatial operator/function” is a function is that configured to operate on spatial data types. Certain types of spatial functions can determine topological relationships between various spatial data instances. Other spatial functions not listed below can also be supported. 
     
       
         
           
               
               
             
               
                   
               
               
                 Operator 
                 Description 
               
               
                   
               
             
            
               
                 ANYINTERACT 
                 Checks if any geometries have the ANYINTERACT topological 
               
               
                   
                 relationship with a specified geometry. 
               
               
                 CONTAINS 
                 Checks if any geometries have the CONTAINS topological 
               
               
                   
                 relationship with a specified geometry. 
               
               
                 INSIDE 
                 Checks if any geometries have the INSIDE topological 
               
               
                   
                 relationship with a specified geometry. 
               
               
                 WITHINDISTANCE 
                 Determines if two geometries are within a specified distance 
               
               
                   
                 from one another. 
               
               
                 FILTER 
                 Identifies the set of spatial objects that are likely to interact 
               
               
                   
                 spatially with a given object. In one set of embodiments, this is 
               
               
                   
                 performed by scanning a spatial index on the set of spatial 
               
               
                   
                 objects (described in further detail below). 
               
               
                 NN 
                 Determines the nearest neighbor geometries to a geometry. 
               
               
                 COVEREDBY 
                 Checks if any geometries have the COVEREDBY topological 
               
               
                   
                 relationship with a specified geometry. 
               
               
                 COVERS 
                 Checks if any geometries have the COVERS topological 
               
               
                   
                 relationship with a specified geometry. 
               
               
                 EQUAL 
                 Checks if any geometries have the EQUAL topological 
               
               
                   
                 relationship with a specified geometry. 
               
               
                 ON 
                 Checks if any geometries have the ON topological relationship 
               
               
                   
                 with a specified geometry. 
               
               
                 OVERLAPBDYDISJOINT 
                 Checks if any geometries have the OVERLAPBDYDISJOINT 
               
               
                   
                 topological relationship with a specified geometry. 
               
               
                 OVERLAPBDYINTERSECT 
                 Checks if any geometries have the 
               
               
                   
                 OVERLAPBDYINTERSECT topological relationship with a 
               
               
                   
                 specified geometry. 
               
               
                 OVERLAPS 
                 Checks if any geometries overlap (i.e., have the 
               
               
                   
                 OVERLAPBDYDISJOINT or OVERLAPBDYINTERSECT 
               
               
                   
                 topological relationship with) a specified geometry. 
               
               
                 TOUCH 
                 Checks if any geometries have the TOUCH topological 
               
               
                   
                 relationship with a specified geometry. 
               
               
                   
               
            
           
         
       
     
     The following is an example of a CQL query that references a spatial data cartridge named “SPATIAL” and executes the CONTAINS function on two relations comprising spatial data: 
     SELECT * 
     FROM R1, R2 
     WHERE CONTAINS@SPATIAL(R1.polygon, R2.point) 
     The objective of this query is to output one tuple for each (polygon, point) pair from the cross product of relations R1 and R2 where the polygon contains the point. Merely by way of example, each point in R2 can represent the two-dimensional location of an individual, and each polygon in R1 can represent a two-dimensional hazard area (e.g., the location of a fire). Thus, in this example, the result set of the query can identify every individual that is located within a hazard area. 
     As shown, the CQL query includes a link definition (“CONTAINS@SPATIAL”) indicating that the function CONTAINS is an extensible object defined within the data cartridge SPATIAL. In addition, the CQL statement specifies two spatial data types—polygon and point. As described with respect to  FIG. 4  above, event processing system  102  of  FIG. 1  can interact with the SPATIAL data cartridge at compile-time to retrieve metadata for the CONTAINS function and the polygon and point data types. Based on that metadata, executable code/instructions can be generated for the query. Further, as described with respect to  FIG. 5  above, event processing system  102  can execute the generated code/instructions at runtime by invoking a call-out to the CONTAINS function as implemented in the SPATIAL data cartridge. 
     In one set of embodiments, the spatial data cartridge can use a two-tier query model to resolve spatial queries such as the query above. The first tier (referred to as the primary filter) can be used to quickly select candidate records to pass along to the second tier (referred to as the secondary filter). In one embodiment, the primary filter compares geometric approximations to reduce computational complexity, and thus is considered a lower-cost filter. Because the primary filter compares geometric approximations (rather than doing an exact geometric comparison), the filter returns a superset of the exact result set. 
     The secondary filter applies exact computations to the geometries that result from the primary filter. Thus, the secondary filter yields an exact answer to the spatial query. The second filter operation (i.e., performing exact geometric comparisons) can be computationally expensive, but is only applied to the primary filter results, rather than the entire data set. Thus, by using this two-tier query model, spatial operations can be performed in a computational efficient manner. 
     Applying this model to the CQL query example above, at runtime the SPATIAL data cartridge can run a primary filter on geometric approximations of the polygons in relation R1 to quickly determine a superset of polygons that can contain a given point in relation R2. The SPATIAL data cartridge can then perform an exact CONTAINS comparison based on the geometric coordinates of the polygons in the superset and the geometric coordinates of the point, thereby producing an exact result set. 
     In one set of embodiments, the spatial data cartridge can specify an indexing scheme for one or more spatial operators (the techniques for creating such an index are described U.S. Provisional Application No. 61/327,903, filed Apr. 26, 2010, entitled EXTENSIBLE INDEXING FRAMEWORK USING DATA CARTRIDGES, which the present application claims priority to, and which is incorporated herein by reference for all purposes). For example, the spatial data cartridge can instantiate a spatial index on the set of geometries to be searched via the CONTAINS operator. In these embodiments, the spatial index can be used to implement the primary filter. 
     The purpose of the spatial index is to quickly create a subset of the data to be searched and thereby reduce the processing burden on the secondary filter (i.e., where exact geometric comparisons are performed). A spatial index, like any other index, provides a mechanism to limit searches, but in the case of the spatial data cartridge the mechanism is based on spatial criteria such as intersection and containment. 
     In one set of embodiments, the spatial data cartridge can use R-Tree indexing as its default indexing mechanism. A spatial R-Tree index can index spatial data of up to four dimensions. In a particular embodiment, an R-Tree index can approximate each geometry by a single rectangle that minimally encloses the geometry (referred to as the minimum bounding rectangle, or MBR). 
     Returning to the CQL query example above, at compile-time, compiler  134  of event processing system  102  can determine (based on the metadata retrieved from the SPATIAL data cartridge) than an indexing scheme is provided for the CONTAINS operator, and thus compiler  134  can create an index-based execution plan for executing the query. In addition, an R-Tree index can be instantiated by the SPATIAL data cartridge for storing the polygons belonging to relation R1. 
     At runtime, when a new polygon is added to R1, the SPATIAL data cartridge can insert the new polygon into the R-Tree index instance. As part of this insertion process, the polygon can be approximated using a minimum bounding rectangle. When a new point is added to R2, the SPATIAL data cartridge can scan the index on R1 to identify all polygons in R1 that contain the newly added point (i.e., the primary filter). The results of the index scan can then be used to perform an exact CONTAINS comparison based on the geometric coordinates of the polygons in the superset and the geometric coordinates of the point, thereby producing an exact result set (i.e., the secondary filter). 
       FIG. 6  is a flow diagram of a process  600  performed by a spatial data cartridge for executing a spatial function in a query according to an embodiment of the present invention. Process  600  can be implemented in software, hardware, or a combination of both. As software, process  600  can be stored on a non-transitory computer-readable medium. In a particular embodiment, the spatial data cartridge can correspond to data cartridge  132  depicted in  FIGS. 1-3 . 
     At block  602 , the spatial data cartridge can receive, from runtime engine  136  of event processing system  102 , an invocation of a spatial function implemented in the data cartridge. For example, this invocation can be received in response to a call-out performed by runtime engine  136  during query execution as shown at block  506  of  FIG. 5 . 
     At block  604 , the spatial data cartridge can execute a primary filter operation on the input arguments to the function (e.g., input streams and/or relations) to determine a non-exact result set for the function. For example, if the function is a geometric CONTAINS function performed on a relation of polygons and a point, the primary filter can determine, based on geometric approximations of the polygons, a group of candidate polygons that is likely contain the point. Note that this group of candidate polygons is a superset of the exact result set for the CONTAINS function, since some of the candidate polygons may not, in fact, contain the point. The purpose of the primary filter is to prune the search space (in this case, the relation of polygons) using a computationally inexpensive operation, so that more expensive geometric comparisons can be subsequently performed on the smaller group of candidate polygons (rather than the entire relation). 
     As discussed above, in certain embodiments the primary filter can be implemented using a spatial index. For example, a spatial index can be defined for the relation of polygons, where the geometry of each polygon in the index is approximated using, e.g., a minimum bounding rectangle. In this embodiment, executing the primary filter can comprise performing a index scan on the index to identify all polygons that include the point. Since the geometries of the polygons are approximated rather than exact, the result set returned by the index scan will be a superset of the exact result set. 
     Once the primary filter has been executed, the spatial data cartridge can execute a secondary filter operation on the non-exact results returned by the primary filter, thereby resulting in a exact result for the spatial function (blocks  606 .  608 ). For instance, returning to the example above, executing the secondary filter operation can include performing the CONTAINS operation based on the exact geometric coordinates of the candidate polygons returned by the primary filter, and the geometric coordinates of the point. This yields an exact result set for the CONTAINS function. Although the secondary filter operation (i.e., performing exact geometric comparisons) can be computationally expensive, since it only applied to the primary filter results, the overall cost for executing the spatial function is reduced. 
     It should be appreciated that process  600  is illustrative and that variations and modifications are possible. Steps described as sequential can be executed in parallel, order of steps can be varied, and steps can be modified, combined, added, or omitted. One of ordinary skill in the art will recognize many variations, modifications, and alternatives. 
     In one set of embodiments, the spatial data cartridge can specify application context information that is used during runtime processing of spatial queries. For example, when performing spatial data analysis, a specific geometric coordinate system usually needs to be specified. Different spatial data applications can use different coordinate systems. By predefining this type of information in an application context, this information can be accessible to the application when creating new spatial data object instances or invoking spatial functions. This context information can be configured by a developer of the spatial data cartridge and stored in a data file, such as an Event Processing Network (EPN) assembly file. 
     The following table identifies attributes that can be specified in a spatial application context according to an embodiment of the present invention. Other attributes not listed below can also be supported. 
     
       
         
           
               
               
             
               
                   
               
               
                 Attribute 
                 Description 
               
               
                   
               
             
            
               
                 anyinteract- 
                 The default tolerance for contain or inside operator. 
               
               
                 tolerance 
                 Default: 0.0000005 
               
               
                 rof 
                 Defines the Reciprocal of Flattening (ROF) parameter used 
               
               
                   
                 for buffering and projection. Default: 298.257223563 
               
               
                 sma 
                 Defines the Semi-Major axis (SMA) parameter used for 
               
               
                   
                 buffering and projection. Default: 6378137.0 
               
               
                 srid 
                 SRID integer (identifies coordinate system). Valid values 
               
               
                   
                 are: 
               
               
                   
                 CARTESIAN: for Cartesian coordinate system. 
               
               
                   
                 LAT_LNG_WGS84_SRID: for WGS84 coordinate 
               
               
                   
                 system. 
               
               
                   
                 An integer value from the Oracle Spatial 
               
               
                   
                 SDO_COORD_SYS table 
               
               
                   
                 COORD_SYS_ID column. 
               
               
                   
                 Default: LAT_LNG_WGS84_SRID 
               
               
                 Tolerance 
                 The minimum distance to be ignored in geometric 
               
               
                   
                 operations including buffering. Default: 0.000000001 
               
               
                   
               
            
           
         
       
     
       FIG. 7  is a simplified block diagram illustrating components of a system environment  700  that can be used in accordance with an embodiment of the present invention. As shown, system environment  700  includes one or more client computing devices  702 ,  704 ,  706 ,  708 , which are configured to operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like. In various embodiments, client computing devices  702 ,  704 ,  706 , and  708  can interact with an event processing system such as system  712 . 
     Client computing devices  702 ,  704 ,  706 ,  708  can be general purpose personal computers (including, by way of example, personal computers and/or laptop computers running various versions of Microsoft Windows and/or Apple Macintosh operating systems), cell phones or PDAs (running software such as Microsoft Windows Mobile and being Internet, e-mail, SMS, Blackberry, or other communication protocol enabled), and/or workstation computers running any of a variety of commercially-available UNIX or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems). Alternatively, client computing devices  702 ,  704 ,  706 , and  708  can be any other electronic device, such as a thin-client computer, Internet-enabled gaming system, and/or personal messaging device, capable of communicating over a network (e.g., network  710  described below). Although exemplary system environment  700  is shown with four client computing devices, any number of client computing devices can be supported. 
     System environment  700  can include a network  710 . Network  710  can be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation TCP/IP, SNA, IPX, AppleTalk, and the like. Merely by way of example, network  710  can be a local area network (LAN), such as an Ethernet network, a Token-Ring network and/or the like; a wide-area network; a virtual network, including without limitation a virtual private network (VPN); the Internet; an intranet; an extranet; a public switched telephone network (PSTN); an infra-red network; a wireless network (e.g., a network operating under any of the IEEE 802.11 suite of protocols, the Bluetooth protocol known in the art, and/or any other wireless protocol); and/or any combination of these and/or other networks. 
     Event processing system  712  can comprise one or more server computers which can be general purpose computers, specialized server computers (including, by way of example, PC servers, UNIX servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, or any other appropriate arrangement and/or combination. In various embodiments, system  712  can be adapted to run one or more services or software applications described in the foregoing disclosure. 
     System  712  can run an operating system including any of those discussed above, as well as any commercially available server operating system. System  712  can also run any of a variety of additional server applications and/or mid-tier applications, including HTTP servers, FTP servers, CGI servers, Java servers, database servers, and the like. Exemplary database servers include without limitation those commercially available from Oracle, Microsoft, Sybase, IBM and the like. 
     System environment  700  can also include one or more databases  714  and  716 . Databases  714  and  716  can reside in a variety of locations. By way of example, one or more of databases  714  and  716  can reside on a storage medium local to (and/or resident in) system  712 . Alternatively, databases  714  and  716  can be remote from system  712 , and in communication with system  712  via a network-based or dedicated connection. In one set of embodiments, databases  714  and  716  can reside in a storage-area network (SAN) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to system  712  can be stored locally on system  712  and/or remotely, as appropriate. In one set of embodiments, databases  714  and  716  can include relational databases, such as Oracle 10g, which are adapted to store, update, and retrieve data in response to SQL-formatted commands. 
       FIG. 8  is a simplified block diagram of a computer system  800  that can be used in accordance with embodiments of the present invention. For example, system  800  can be used to implement event processing system  102  depicted in  FIGS. 1 and 3 . Computer system  800  is shown comprising hardware elements that can be electrically coupled via a bus  824 . The hardware elements can include one or more central processing units (CPUs)  802 , one or more input devices  804  (e.g., a mouse, a keyboard, etc.), and one or more output devices  806  (e.g., a display device, a printer, etc.). Computer system  800  can also include one or more storage devices  808 . By way of example, the storage device(s)  808  can include devices such as disk drives, optical storage devices, and solid-state storage devices such as a random access memory (RAM) and/or a read-only memory (ROM), which can be programmable, flash-updateable and/or the like. 
     Computer system  800  can additionally include a computer-readable storage media reader  812 , a communications subsystem  814  (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.), and working memory  818 , which can include RAM and ROM devices as described above. In some embodiments, computer system  800  can also include a processing acceleration unit  816 , which can include a digital signal processor (DSP), a special-purpose processor, and/or the like. 
     Computer-readable storage media reader  812  can further be connected to a computer-readable storage medium  810 , together (and, optionally, in combination with storage device(s)  808 ) comprehensively representing remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing computer-readable information. Communications subsystem  814  can permit data to be exchanged with network  710  and/or any other computer described above with respect to system environment  700 . 
     Computer system  800  can also comprise software elements, shown as being currently located within working memory  818 , including an operating system  820  and/or other code  822 , such as an application program (which can be a client application, Web browser, mid-tier application, RDBMS, etc.). In an exemplary embodiment, working memory  818  can include executable code and associated data structures (such as caches) used for processing events and performing data cartridge-related processing as described above. It should be appreciated that alternative embodiments of computer system  800  can have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices can be employed. 
     Storage media and computer readable media for containing code, or portions of code, can include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information such as computer readable instructions, data structures, program modules, or other data, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store or transmit the desired information and which can be accessed by a computer. 
     Although specific embodiments of the invention have been described, various modifications, alterations, alternative constructions, and equivalents are also encompassed within the scope of the invention. Embodiments of the present invention are not restricted to operation within certain specific data processing environments, but are free to operate within a plurality of data processing environments. Additionally, although embodiments of the present invention have been described using a particular series of transactions and steps, it should be apparent to those skilled in the art that the scope of the present invention is not limited to the described series of transactions and steps. 
     Further, while embodiments of the present invention have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are also within the scope of the present invention. Embodiments of the present invention can be implemented only in hardware, or only in software, or using combinations thereof. 
     The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that additions, subtractions, deletions, and other modifications and changes can be made thereunto without departing from the broader spirit and scope as set forth in the claims.